Expanding a hierarchical dispersed storage index

ABSTRACT

A method begins by a dispersed storage (DS) processing module determining to expand a hierarchical ordered index structure and retrieving a root index node. The method continues with the DS processing module identifying immediate children index nodes, dividing the immediate children index nodes into sets of children index nodes, creating, for each of the sets of children index nodes, a sub-root index node to produce a set of sub-root index nodes, creating a new root index node to include entries for each of the sub-root index nodes of the set of sub-root index nodes, and temporarily storing the new root index node and the set of sub-root index nodes in a dispersed storage network (DSN). When the root index node has not changed, the method continues with the DS processing module updating the hierarchical ordered index structure with the new root index node and the set of sub-root index nodes.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 61/605,856,entitled “Utilizing an Index of a Distributed Storage and Task Network”filed Mar. 2, 2012, pending, which is incorporated herein by referencein its entirety and made part of the present U.S. Utility patentapplication for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION

1. Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

2. Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work station, video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a diagram illustrating an example of an index structure inaccordance with the present invention;

FIG. 40B is a diagram illustrating an example of an index node structurein accordance with the present invention;

FIG. 40C is a diagram illustrating an example of a leaf node structurein accordance with the present invention;

FIG. 40D is a diagram illustrating another example of an index structurein accordance with the present invention;

FIG. 40E is a flowchart illustrating an example of searching an indexstructure in accordance with the present invention;

FIG. 41A is a diagram illustrating another example of an index structurein accordance with the present invention;

FIG. 41B is a schematic block diagram of an embodiment of a dispersedstorage system in accordance with the present invention;

FIG. 41C is a flowchart illustrating an example of listing an indexstructure in accordance with the present invention;

FIG. 42A is a diagram illustrating an example of an index metadatastructure in accordance with the present invention;

FIG. 42B is a flowchart illustrating an example of identifying an indexin accordance with the present invention;

FIG. 43A is a schematic block diagram of another embodiment of adispersed storage system in accordance with the present invention;

FIG. 43B is a flowchart illustrating an example of modifying an index inaccordance with the present invention;

FIG. 44A is a diagram illustrating another example of an index structurein accordance with the present invention;

FIG. 44B is a diagram illustrating another example of an index structurein accordance with the present invention;

FIG. 44C is a diagram illustrating an example of an index structure of astarting step of a series of example steps depicted in FIGS. 44D through44J in accordance with the present invention;

FIG. 44D is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J in accordance with the present invention;

FIG. 44E is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J in accordance with the present invention;

FIG. 44F is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J in accordance with the present invention;

FIG. 44G is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J in accordance with the present invention;

FIG. 44H is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J in accordance with the present invention;

FIG. 44J is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J in accordance with the present invention;

FIG. 44K is a schematic block diagram of another embodiment of adispersed storage system in accordance with the present invention;

FIG. 44L is a flowchart illustrating an example of joining nodes of anindex in accordance with the present invention;

FIG. 45A is a diagram illustrating another example of an index structurein accordance with the present invention;

FIG. 45B is a diagram illustrating another example of an index structurein accordance with the present invention;

FIG. 45C is a diagram illustrating an example of an index structure of astarting step of a series of example steps depicted in FIGS. 44C and 44Din accordance with the present invention;

FIG. 45D is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44C and44D in accordance with the present invention;

FIG. 44E is a schematic block diagram of another embodiment of adispersed storage system in accordance with the present invention;

FIG. 45F is a flowchart illustrating an example of splitting nodes of anindex in accordance with the present invention;

FIG. 46A is a diagram illustrating another example of an index structurein accordance with the present invention;

FIG. 46B is a diagram illustrating another example of an index structurein accordance with the present invention;

FIG. 46C is a diagram illustrating an example of an index structure ofan example of expanding the index structure in example steps depicted inFIGS. 46D and 46E in accordance with the present invention;

FIG. 46D is a diagram illustrating an example of an index structure of astarting step of a series of example steps of expanding the indexstructure depicted in FIGS. 46D and 46E in accordance with the presentinvention;

FIG. 46E is a diagram illustrating an example of the index structure ofanother step of the series of example steps of expanding the indexstructure depicted in FIGS. 46D and 46E in accordance with the presentinvention;

FIG. 46F is a diagram illustrating an example of expanding an index inaccordance with the present invention;

FIG. 46G is a schematic block diagram of another embodiment of adispersed storage system in accordance with the present invention;

FIG. 46H is a flowchart illustrating an example of expanding an index inaccordance with the present invention;

FIG. 47 is a flowchart illustrating an example of acquiring operationalsoftware in accordance with the present invention;

FIG. 48A is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 48B is a flowchart illustrating an example of issuing a softwareimage update in accordance with the present invention;

FIG. 48C is a flowchart illustrating an example of receiving a softwareimage update in accordance with the present invention;

FIG. 49A is a flowchart illustrating an example of preparing for anupgrade in accordance with the present invention;

FIG. 49B is a flowchart illustrating an example of verifying an upgradein accordance with the present invention;

FIG. 50A is a flowchart illustrating an example of migrating an encodeddata slice in accordance with the present invention;

FIG. 50B is a flowchart illustrating an example of saving a migratedencoded data slice in accordance with the present invention;

FIG. 51A is a diagram illustrating an example of a registry structure inaccordance with the present invention; and

FIG. 51B is a flowchart illustrating an example of distributing Registryinformation in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schfigureematic block diagram of an embodiment of adistributed computing system 10 that includes a user device 12 and/or auser device 14, a distributed storage and/or task (DST) processing unit16, a distributed storage and/or task network (DSTN) managing unit 18, aDST integrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interfaces 30support a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g. or dispersed storage error codingparameters) include data segmenting information (e.g., how many segmentsdata (e.g., a file, a group of files, a data block, etc.) is dividedinto), segment security information (e.g., per segment encryption,compression, integrity checksum, etc.), error coding information (e.g.,pillar width, decode threshold, read threshold, write threshold, etc.),slicing information (e.g., the number of encoded data slices that willbe created for each data segment); and slice security information (e.g.,per encoded data slice encryption, compression, integrity checksum,etc.).

The DSTN managing module 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsincludes, but is not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terra-Bytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terra-Bytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terra-Bytes) into 100,000 data segments, each being 1 Giga-Byte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The group selecting module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the group selecting modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The group selecting module 114 outputs the slice groupings96 to the corresponding DST execution units 36 via the network 24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, wherein in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or and the other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of datasegments 156 for a given data partition, the slicing module outputs aplurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_(—)1and ES1_(—)2) of the first set of encoded data slices include errorcorrection data based on the first-third words of the first datasegment. With such an encoding and slicing scheme, retrieving any threeof the five encoded data slices allows the data segment to be accuratelyreconstructed.

The encoding and slices of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_(—)1and ES1_(—)2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selection module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selection module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selection module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selection module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selection module 114 creates a fourth slice grouping forDST execution unit #4, which includes fourth encoded slices of each ofthe sets of encoded slices. As such, the fourth DST execution unitreceives encoded data slices corresponding to first error encodinginformation (e.g., encoded data slices of error coding (EC) data). Thegrouping selection module 114 further creates a fifth slice grouping forDST execution unit #5, which includes fifth encoded slices of each ofthe sets of encoded slices. As such, the fifth DST execution unitreceives encoded data slices corresponding to second error encodinginformation.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_(—)1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_(—)2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_(—)3) is sent to the fourth DST execution unit; the fourth slicegrouping of the second data partition (e.g., slice group 2_(—)4, whichincludes first error coding information) is sent to the fifth DSTexecution unit; and the fifth slice grouping of the second datapartition (e.g., slice group 2_(—)5, which includes second error codinginformation) is sent to the first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or andthe other analog and/or digital processing circuitry), availability ofthe processing resources, etc. If the controller 86 determines that theDT execution module(s) 90 have sufficient capabilities, it generatestask control information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results may 102 also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identity of other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 andthe memory 88. For example, when the partial task 98 includes aretrieval request, the controller 86 outputs the memory control 174 tothe memory 88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step executing the task in accordance with the task controlinformation 176, the DT execution module 90 retrieves the encoded slicesfrom memory 88. The DT execution module 90 then reconstructs contiguousdata blocks of a data partition. As shown for this example,reconstructed contiguous data blocks of data partition 1 include datablocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify wherein thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbounded DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieve slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_(—)1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of a de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupselection module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The group selecting module 114 groups the encoded slices 218 of the datasegments into pillars of slices 216. The number of pillars correspondsto the pillar width of the DS error encoding parameters. In thisexample, the distributed task control module 118 facilitates the storagerequest.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Salomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selection module organizes the sets of encodeddata slices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selection module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selection module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Salomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terra-Bytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distributions modules location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few terra-bytes or more),addressing information of Addr_(—)1_AA, and DS parameters of 3/5;SEG_(—)1; and SLC_(—)1. In this example, the addressing information maybe a virtual address corresponding to the virtual address of the firststorage word (e.g., one or more bytes) of the data and information onhow to calculate the other addresses, may be a range of virtualaddresses for the storage words of the data, physical addresses of thefirst storage word or the storage words of the data, may be a list ofslices names of the encoded data slices of the data, etc. The DSparameters may include identity of an error encoding scheme, decodethreshold/pillar width (e.g., 3/5 for the first data entry), segmentsecurity information (e.g., SEG_(—)1), per slice security information(e.g., SLC_(—)1), and/or any other information regarding how the datawas encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_(—)2_XY, andDS parameters of 3/5; SEG_(—)2; and SLC_(—)2. In this example, theaddressing information may be a virtual address corresponding to thevirtual address of the first storage word (e.g., one or more bytes) ofthe task and information on how to calculate the other addresses, may bea range of virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_(—)2), per slice securityinformation (e.g., SLC_(—)2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and asub-task field 258. The task field 256 identifies a task stored in thememory of a distributed storage and task network (DSTN) module and thecorresponding sub-task fields 258 indicates whether the task includessub-tasks and, if so, how many and if any of the sub-tasks are ordered.In this example, the task

sub-task mapping information table 246 includes an entry for each taskstored in memory of the DSTN module (e.g., task 1 through task k). Inparticular, this example indicates that task 1 includes 7 sub-tasks;task 2 does not include sub-tasks, and task k includes r number ofsub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_(—)1, 1_(—)2, and 1_(—)3). The DT execution capabilities field280 includes identity of the capabilities of the corresponding DTexecution unit. For example, DT execution module 1_(—)1 includescapabilities X, where X includes one or more of MIPS capabilities,processing resources (e.g., quantity and capability of microprocessors,CPUs, digital signal processors, co-processor, microcontrollers,arithmetic logic circuitry, and/or and other analog and/or digitalprocessing circuitry), availability of the processing resources, memoryinformation (e.g., type, size, availability, etc.), and/or anyinformation germane to executing one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words or/or phrases intranslated data.

In this example, task 1 includes 7 sub-tasks: task 1_(—)1—identifynon-words (non-ordered); task 1_(—)2—identify unique words(non-ordered); task 1_(—)3—translate (non-ordered); task1_(—)4—translate back (ordered after task 1_(—)3); task 1_(—)5—compareto ID errors (ordered after task 1-4); task 1_(—)6—determine non-wordtranslation errors (ordered after task 1_(—)5 and 1_(—)1); and task1_(—)7—determine correct translations (ordered after 1_(—)5 and 1_(—)2).The sub-task further indicates whether they are an ordered task (i.e.,are dependent on the outcome of another task) or non-order (i.e., areindependent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_(—)1 translate; andtask 3_(—)2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_(—)2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases. The translated data 282 is translatedback 308 (e.g., sub-task 1_(—)4) into the language of the original datato produce re-translated data 284. These two tasks are dependent on thetranslate task (e.g., task 1_(—)3) and thus must be ordered after thetranslation task, which may be in a pipelined ordering or a serialordering. The re-translated data 284 is then compared 310 with theoriginal data 92 to find words and/or phrases that did not translate(one way and/or the other) properly to produce a list of incorrectlytranslated words 294. As such, the comparing task (e.g., sub-task1_(—)5) 310 is ordered after the translation 306 and re-translationtasks 308 (e.g., sub-tasks 1_(—)3 and 1_(—)4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done the by DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_(—)1 through 2_z) and addressing information foreach partition. The task distribution module generates an entry in thetask execution information section for each sub-task to be performed.For example, task 1_(—)1 (e.g., identify non-words on the data) has notask ordering (i.e., is independent of the results of other sub-tasks),is to be performed on data partitions 2_(—)1 through 2_z by DT executionmodules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1. For instance, DTexecution modules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 search fornon-words in data partitions 2_(—)1 through 2_z to produce task 1_(—)1intermediate results (R1-1, which is a list of non-words). Task 1_(—)2(e.g., identify unique words) has similar task execution information astask 1_(—)1 to produce task 1_(—)2 intermediate results (R1-2, which isthe list of unique words).

Task 1_(—)3 (e.g., translate) includes task execution information asbeing non-ordered (i.e., is independent), having DT execution modules1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 translate data partitions2_(—)1 through 2_(—)4 and having DT execution modules 1_(—)2, 2_(—)2,3_(—)2, 4_(—)2, and 5_(—)2 translate data partitions 2_(—)5 through 2_zto produce task 1_(—)3 intermediate results (R1-3, which is thetranslated data). In this example, the data partitions are grouped,where different sets of DT execution modules perform a distributedsub-task (or task) on each data partition group, which allows forfurther parallel processing.

Task 1_(—)4 (e.g., translate back) is ordered after task 1_(—)3 and isto be executed on task 1_(—)3's intermediate result (e.g., R1-3_(—)1)(e.g., the translated data). DT execution modules 1_(—)1, 2_(—)1,3_(—)1, 4_(—)1, and 5_(—)1 are allocated to translate back task 1_(—)3intermediate result partitions R1-3_(—)1 through R1-3_(—)4 and DTexecution modules 1_(—)2, 2_(—)2, 6_(—)1, 7_(—)1, and 7_(—)2 areallocated to translate back task 1_(—)3 intermediate result partitionsR1-3_(—)5 through R1-3_z to produce task 1-4 intermediate results (R1-4,which is the translated back data).

Task 1_(—)5 (e.g., compare data and translated data to identifytranslation errors) is ordered after task 1_(—)4 and is to be executedon task 1_(—)4's intermediate results (R4-1) and on the data. DTexecution modules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 areallocated to compare the data partitions (2_(—)1 through 2_z) withpartitions of task 1-4 intermediate results partitions R1-4_(—)1 throughR1-4_z to produce task 1_(—)5 intermediate results (R1-5, which is thelist words translated incorrectly).

Task 1_(—)6 (e.g., determine non-word translation errors) is orderedafter tasks 1_(—)1 and 1_(—)5 and is to be executed on tasks 1_(—)1'sand 1_(—)5's intermediate results (R1-1 and R1-5). DT execution modules1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 are allocated to compare thepartitions of task 1_(—)1 intermediate results (R1-1_(—)1 throughR1-1_z) with partitions of task 1-5 intermediate results partitions(R1-5_(—)1 through R1-5_z) to produce task 1_(—)6 intermediate results(R1-6, which is the list translation errors due to non-words).

Task 1_(—)7 (e.g., determine words correctly translated) is orderedafter tasks 1_(—)2 and 1_(—)5 and is to be executed on tasks 1_(—)2'sand 1_(—)5's intermediate results (R1-1 and R1-5). DT execution modules1_(—)2, 2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2 are allocated to compare thepartitions of task 1_(—)2 intermediate results (R1-2_(—)1 throughR1-2_z) with partitions of task 1-5 intermediate results partitions(R1-5_(—)1 through R1-5_z) to produce task 1_(—)7 intermediate results(R1-7, which is the list of correctly translated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_(—)1 through 2_z by DT execution modules3_(—)1, 4_(—)1, 5_(—)1, 6_(—)1, and 7_(—)1. For instance, DT executionmodules 3_(—)1, 4_(—)1, 5_(—)1, 6_(—)1, and 7_(—)1 search for specificwords and/or phrases in data partitions 2_(—)1 through 2_z to producetask 2 intermediate results (R2, which is a list of specific wordsand/or phrases).

Task 3_(—)2 (e.g., find specific translated words and/or phrases) isordered after task 1_(—)3 (e.g., translate) is to be performed onpartitions R1-3_(—)1 through R1-3_z by DT execution modules 1_(—)2,2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2. For instance, DT execution modules1_(—)2, 2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2 search for specifictranslated words and/or phrases in the partitions of the translated data(R1-3_(—)1 through R1-3_z) to produce task 3_(—)2 intermediate results(R3-2, which is a list of specific translated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_(—)1), DST unit 1 isresponsible for overseeing execution of the task 1_(—)1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1 _(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 per the DSTallocation information of FIG. 32) executes task 1_(—)1 to produce afirst partial result 102 of non-words found in the first data partition.The second set of DT execution modules (e.g., 1_(—)1, 2_(—)1, 3_(—)1,4_(—)1, and 5_(—)1 per the DST allocation information of FIG. 32)executes task 1_(—)1 to produce a second partial result 102 of non-wordsfound in the second data partition. The sets of DT execution modules (asper the DST allocation information) perform task 1_(—)1 on the datapartitions until the “z” set of DT execution modules performs task1_(—)1 on the “zth” data partition to produce a “zth” partial result 102of non-words found in the “zth” data partition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_(—)1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g.,R1-1_(—)1 through R1-1_m). If the first intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_(—)2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_(—)1 if the partitioning is the same. For each data partition, theDSTN identifies a set of its DT execution modules to perform task 1_(—)2in accordance with the DST allocation information. From data partitionto data partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_(—)2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_(—)2 to produce the second intermediate result (R1-2),which is a list of unique words found in the data 92. The processingmodule of DST execution 1 is engaged to aggregate the first through“zth” partial results of unique words to produce the second intermediateresult. The processing module stores the second intermediate result asnon-DS error encoded data in the scratchpad memory or in another sectionof memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g.,R1-2_(—)1 through R1-2_m). If the second intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_(—)3 (e.g., translate)on the data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task1_(—)1 if the partitioning is the same. For each data partition, theDSTN identifies a set of its DT execution modules to perform task 1_(—)3in accordance with the DST allocation information (e.g., DT executionmodules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1 translate datapartitions 2_(—)1 through 2_(—)4 and DT execution modules 1_(—)2,2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2 translate data partitions 2_(—)5through 2_z). For the data partitions, the allocated set of DT executionmodules 90 executes task 1_(—)3 to produce partial results 102 (e.g.,1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_(—)3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g.,R1-3_(—)1 through R1-3_y). For each partition of the third intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35, the DSTN module is performing task1_(—)4 (e.g., retranslate) on the translated data of the thirdintermediate result. To begin, the DSTN module accesses the translateddata (from the scratchpad memory or from the intermediate result memoryand decodes it) and partitions it into a plurality of partitions inaccordance with the DST allocation information. For each partition ofthe third intermediate result, the DSTN identifies a set of its DTexecution modules 90 to perform task 1_(—)4 in accordance with the DSTallocation information (e.g., DT execution modules 1_(—)1, 2_(—)1,3_(—)1, 4_(—)1, and 5_(—)1 are allocated to translate back partitionsR1-3_(—)1 through R1-3_(—)4 and DT execution modules 1_(—)2, 2_(—)2,6_(—)1, 7_(—)1, and 7_(—)2 are allocated to translate back partitionsR1-3_(—)5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_(—)4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_(—)4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_(—)1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_(—)5 (e.g., compare) on data 92 and retranslated dataof FIG. 35. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions in accordance with the DSTallocation information or it may use the data partitions of task 1_(—)1if the partitioning is the same. The DSTN module also accesses theretranslated data from the scratchpad memory, or from the intermediateresult memory and decodes it, and partitions it into a plurality ofpartitions in accordance with the DST allocation information. The numberof partitions of the retranslated data corresponds to the number ofpartitions of the data.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_(—)5 in accordance with the DST allocationinformation (e.g., DT execution modules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1,and 5_(—)1). For each pair of partitions, the allocated set of DTexecution modules executes task 1_(—)5 to produce partial results 102(e.g., 1^(st) through “zth”) of a list of incorrectly translated wordsand/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_(—)5 to produce the fifth intermediate result (R1-5), which isthe list of incorrectly translated words and/or phrases. In particular,the processing module of DST execution 1 is engaged to aggregate thefirst through “zth” partial results of the list of incorrectlytranslated words and/or phrases to produce the fifth intermediateresult. The processing module stores the fifth intermediate result asnon-DS error encoded data in the scratchpad memory or in another sectionof memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_(—)1 through R1-5_z).For each partition of the fifth intermediate result, the DST clientmodule uses the DS error encoding parameters of the data (e.g., DSparameters of data 2, which includes 3/5 decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task1_(—)6 (e.g., translation errors due to non-words) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of non-words (e.g., the firstintermediate result R1-1). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_(—)1 and partitionR1-5_(—)1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_(—)6 in accordance with the DST allocation information(e.g., DT execution modules 1_(—)1, 2_(—)1, 3_(—)1, 4_(—)1, and 5_(—)1).For each pair of partitions, the allocated set of DT execution modulesexecutes task 1_(—)6 to produce partial results 102 (e.g., 1^(st)through “zth”) of a list of incorrectly translated words and/or phrasesdue to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_(—)6 to produce the sixth intermediate result (R1-6), which isthe list of incorrectly translated words and/or phrases due tonon-words. In particular, the processing module of DST execution 2 isengaged to aggregate the first through “zth” partial results of the listof incorrectly translated words and/or phrases due to non-words toproduce the sixth intermediate result. The processing module stores thesixth intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_(—)1 through R1-6_z).For each partition of the sixth intermediate result, the DST clientmodule uses the DS error encoding parameters of the data (e.g., DSparameters of data 2, which includes 3/5 decode threshold/pillar widthratio) to produce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_(—)7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_(—)1 and partitionR1-5_(—)1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_(—)7 in accordance with the DST allocation information(e.g., DT execution modules 1_(—)2, 2_(—)2, 3_(—)2, 4_(—)2, and 5_(—)2).For each pair of partitions, the allocated set of DT execution modulesexecutes task 1_(—)7 to produce partial results 102 (e.g., 1^(st)through “zth”) of a list of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_(—)7 to produce the seventh intermediate result (R1-7), which isthe list of correctly translated words and/or phrases. In particular,the processing module of DST execution 3 is engaged to aggregate thefirst through “zth” partial results of the list of correctly translatedwords and/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_(—)1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_(—)1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7. DSTexecution unit 7 engages its DST client module to slice grouping basedDS error encode the task 2 intermediate result. To begin the encoding,the DST client module determines whether the list of specific wordsand/or phrases is of a sufficient size to partition (e.g., greater thana Terra-Byte). If yes, it partitions the task 2 intermediate result (R2)into a plurality of partitions (e.g., R2_(—)1 through R2_m). If the task2 intermediate result is not of sufficient size to partition, it is notpartitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terra-Byte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_(—)1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a diagram illustrating an example of a distributed indexstructure 350 of one or more indexes utilized to access a data object ofone or more data objects 1_(—)1 through 1_w, 3_(—)1 through 3_w, 4_(—)1through 4_w, etc., where at least some of the one or more data objectsare stored in at least one of a distributed storage and task network(DSTN) and a dispersed storage network (DSN), and where a data object ofthe one or more data objects is dispersed storage error encoded toproduce a plurality sets of encoded data slices, and where the pluralityof sets of encoded data slices are stored in the DSN (e.g., and/or DSTN)utilizing a common source name (e.g., DSN address). The source nameprovides a DSTN and/or DSN address including one or more of a vaultidentifier (ID) (e.g., such a vault ID associates a portion of storageresources of the DSN with one or more DSN user devices), a vaultgeneration indicator (e.g., identify a vault generation of one or moreof generations), and an object number that corresponds to the dataobject (e.g., a random number assigned to the data object when the dataobject is stored in the DSN).

The distributed index structure 350 includes at least two nodesrepresented in the index structure as nodes associated with two or morenode levels. One or more nodes of the at least two nodes of thedistributed index structure 350 may be dispersed storage error encodedto produce one or more sets of encoded index slices. The one or moresets of encoded index slices may be stored in at least one of a localmemory, a DSN memory, and a distributed storage and task network (DSTN)module. For example, each node of a 100 node distributed index structureare individually dispersed storage error encoded to produce at least 100sets of encoded index slices for storage in the DSTN module. As anotherexample, the 100 node index structure is aggregated into one index fileand the index file is dispersed storage error encoded to produce a setof encoded index slices for storage in the DTSN module.

Each node of the at least two nodes includes at least one of an indexnode and a leaf node. One index node of the at least two nodes includesa root index node. Alternatively, the distributed index structure 350includes just one node, wherein the one node is a leaf node and wherethe leaf node is a root node. The distributed index structure 350 mayinclude any number of index nodes, any number of leaf nodes, and anynumber of node levels. Each level of the any number of node levelsincludes nodes of a common node type. For example, all nodes of nodelevel 4 are leaf nodes and all nodes of node level 3 are index nodes. Asanother example, as illustrated, the distributed index structure 350includes eight index nodes and eight leaf nodes, where the eight indexnodes are organized in three node levels, where a first node levelincludes a root index node 1_(—)1, a second node level includes indexnodes 2_(—)1, 2_(—)2, and 2_(—)3, and a third node level includes indexnodes 3_(—)1, 3_(—)2, 3_(—)3, 3_(—)4, and 3_(—)5, and where the eightleaf nodes are organized in a last (e.g., fourth) node level, where thelast node level includes leaf nodes 4_(—)1, 4_(—)2, 4_(—)3, 4_(—)4,4_(—)5, 4_(—)6, 4_(—)7, and 4_(—)8.

Each data object of the one or more data objects is associated with atleast one index key per distributed index structure of the one or moredistributed indexes, where the index key includes a searchable elementof the distributed index and may be utilized to locate the data objectin accordance with key type traits. An index key type of an index keyincludes a category of the index key (e.g. string integer, etc.). Anindex key type exhibits traits. Each index key is associated with one ormore key type traits (e.g., for an associated index structure), where akey type traits includes one or more of a type indicator, a traitindicator, a comparing function (e.g., defining how an associate indexkey of this type should be compared, such as sorting and/ormanipulation, to other such index keys), a serialization function (e.g.,encoding function for storage), a de-serialization function (e.g.,decoding function for retrieval), and an absolute minimum value of theindex key.

Each leaf node of the at least two nodes may be associated with one ormore data objects. The association includes at least one of, for eachdata object of the one or more data objects, storing an index keyassociated with the data object in the leaf node, storing a source nameassociated with the data object in the leaf node, and storing the dataobject in the leaf node. For example, leaf node 4_(—)2 includes a dataobject 4_(—)2 and an index key associated with data object 4_(—)2. Asanother example, leaf node 4_(—)3 includes source names associated withdata object 3_(—)1 through 3_w and index keys associated with dataobject 3_(—)1 through 3_w. Each leaf node is associated with a minimumindex key, where the minimum index key is a minimum value of one or moreindex keys associated with the one or more data objects in accordancewith the key type traits (e.g., sorted utilizing a comparing function ofthe key type traits to identify the minimum value).

Each leaf node is a child in a parent-child relationship with one indexnode, where the one index node is a parent in the parent-childrelationship. Each child node has one parent node and each parent nodehas one or more child nodes. The one index node (e.g., parent node)stores a minimum index key associated with the leaf node (e.g., childnode). As such, a parent node stores a minimum index key for each childnode of the one or more child nodes. Two index nodes may form aparent-child relationship. In such a parent-child relationship, aparent-child node pair is represented in the index structure with aparent node of the parent-child relationship associated with a parentnode level that is one level above in the index structure than a childnode level associated with a child node of the parent-childrelationship.

A leaf node is a sibling node of another leaf node when a minimum indexkey associated with the leaf node is ordered greater than a last minimumindex key associated with the other leaf node, where the last minimumindex key associated with the leaf node is sorted above any other lastminimum index keys associated with any other lower order leaf nodes andwhere the minimum index key associated with the leaf node is orderedless than any other minimum index keys associated with any other higherorder leaf nodes. A sibling node of a node is represented in the indexstructure on a common level with the node and one node position to theright. A last node on the far right of a node level has a no sibling(e.g., null sibling). All other nodes, if any, other than a last farright node, of a common node level have a sibling node. For example,leaf node 4_(—)2 is a sibling node to leaf node 4_(—)1, leaf node 4_(—)3is a sibling node to leaf node 4_(—)2, etc., leaf node 4_(—)8 is asibling node to leaf node 4_(—)7 and leaf node 4_(—)8 has no siblingnode.

Each index node of the at least two nodes may be associated with one ormore child nodes. Such a child node includes at least one of anotherindex node or a leaf node. The association includes, for each child nodeof the one or more child nodes, storing a minimum index key associatedwith the child node in the index node and storing a source nameassociated with the child node in the index node. Each child node isassociated with a minimum index key, where the minimum index key is aminimum value of one or more index keys associated with the child node(e.g., the minimum index key is a minimum value of one or more indexkeys associated with one or more children nodes of the child node or oneor more data objects of the child node in accordance with the key typetraits, sorted utilizing a comparing function of the key type traits toidentify the minimum value when the child node is a leaf node). Forexample, index node 3_(—)2 includes a minimum index key (e.g., of dataobject 3_(—)1) and source name associated with leaf node 4_(—)3. Asanother example, index node 3_(—)3 includes a minimum index key andsource name associated with leaf node 4_(—)4 and another minimum indexkey and another source name associated with leaf node 4_(—)5. As yetanother example, index node 2_(—)3 includes a minimum index key andsource name associated with index node 3_(—)4 and minimum index key andanother source name associated with index node 3_(—)5.

An index node is a sibling node of another index node when a minimumindex key associated with the index node is ordered greater than a lastminimum index key associated with the other index node, where the lastminimum index key associated with the index node is sorted above anyother last minimum index keys associated with any other lower orderindex nodes and where the minimum index key associated with the indexnode is ordered less than any other minimum index keys associated withany other higher order index nodes. For example, index node 3_(—)2 is asibling node to index node 3_(—)1, index node 3_(—)3 is a sibling nodeto index node 3_(—)2, etc., index node 3_(—)5 is a sibling node to indexnode 3_(—)4 and index node 3_(—)5 has no sibling node.

FIG. 40B is a diagram illustrating an example of an index node structure352 for an index node that includes index node information 356, siblingnode information 358, and children node information 360. Alternatively,there is no sibling node information 358 when the index node has nosibling node. The index node information 356 includes one or more of anindex node source name field 362, an index node revision field 364, anda node type field 366. Inclusion and/or use of the index node sourcename field 362 and the index node revision field 364 is optional.

The sibling node information 358 includes a sibling node source namefield 368, a sibling minimum index key field 370, and a sibling key typetraits field 372. Inclusion and/or use of the sibling key type traitsfield 372 is optional. The children node information 360 includes one ormore child node information sections 374, 376, etc. corresponding toeach child node of the index node. Each child node information sectionof the one or more child node information sections includes acorresponding child node source name field 378, a corresponding childminimum index key field 380, and a corresponding child key type traitsfield 382. For example, the corresponding child node source name field378 of a child 1 node information section 374 includes a child 1 nodesource name entry. Inclusion and/or use of the corresponding child keytype traits field 382 is optional.

The index node source name field 362 may include an index node dispersedstorage network (DSN) address 354 entry (e.g., source name)corresponding to a storage location for the index node. The index noderevision field 364 may include an index node revision entrycorresponding to a revision number of information contained in the indexnode. Use of the index node revision field 364 enables generating two ormore similar indexes while saving each revision of the two or moresimilar indexes. The node type field 366 includes a node type entry,where the node type entry indicates whether the node is a leaf node ornot a leaf node. The node type indicates that the node is not a leafnode when the node is the index node.

The sibling node source name field 368 includes a sibling node sourcename entry (e.g., sibling node DSN address) corresponding to where asibling node is stored in a DSN memory and/or a distributed storage andtask network (DSTN) module when the index node has the sibling node as asibling. The sibling node is another index node when the index node hasthe sibling. The sibling node source name field 368 may include a nullentry when the index node does not have a sibling. The sibling minimumindex key field 370 includes a sibling of minimum index keycorresponding to the sibling node when the index node has the siblingnode as the sibling. The sibling key type traits field 372 may includesibling key type traits corresponding to the sibling node when the indexnode has the sibling node as the sibling and when the sibling key typetraits field is utilized. Alternatively, index structure metadata mayinclude key type traits utilized globally for each node of the indexstructure.

The index structure metadata may include one or more of key type traitsto be utilized for all nodes of a corresponding index, key type traitsto be utilized for all index nodes of the corresponding index, key typetraits to be utilized for all leaf nodes of the corresponding index, asource name of a root node of the index structure, a maximum number ofindex structure levels, a minimum number of the next level structures, amaximum number of elements per index structure level, a minimum numberof elements per index structure level, and index revision number, and anindex name. The index structure metadata may be utilized for one or moreof accessing the index, generating the index, updating the index, savingthe index, deleting portions of the index, adding a portion to theindex, cloning a portion of the index, and searching through the index.The index structure metadata may be stored in one or more of a localmemory, one or more nodes of the index structure, and as encodedmetadata slices in at least one of the DSTN module and the DSN memory.

The child node source name field 378 includes a child node source nameentry (e.g., child node DSN address) corresponding to a storage locationfor the child node. For example, a child 1 node source name field 378 ofa child 1 node information section 374 includes a child 1 node sourcename. The child minimum index key field 380 includes a child minimumindex key corresponding to the child node. For example, a child 1minimum index key field 380 of the child 1 node information section 374includes a child 1 minimum index key. The child key type traits field382 may include child key type traits corresponding to the child nodewhen the index node has the child node as the child and when the childkey type traits field is utilized. Alternatively, the index structuremetadata may include key type traits utilized globally for each node ofthe index structure.

FIG. 40C is a diagram illustrating an example of a leaf node structure384 that includes leaf node information 388, sibling node information358, and data information 392. Alternatively, there is no sibling nodeinformation 358 when the leaf node has no sibling node. The leaf nodeinformation 388 includes one or more of a leaf node source name field394, a leaf node revision field 396, and a node type field 366.Inclusion and/or use of the leaf node source name field 394 and the leafnode revision field 396 is optional. The sibling node information 358includes a sibling node source name field 368, a sibling minimum indexkey field 370, and a sibling key type traits field 372. Inclusion and/oruse of the sibling key type traits field 372 is optional. The datainformation 392 includes one or more data information sections 398, 400,etc. corresponding to each data object associated with the leaf node.Alternatively, the data information 392 includes null information whenno data object is presently associated with the leaf node. Each datainformation section of the one or more data information sectionsincludes a corresponding data (e.g., data object) source name or datafield 402, a corresponding data index key field 404, and a correspondingdata key type traits field 406. For example, the corresponding datasource name field 402 of a data 1 node information section 398 includesa data 1 source name entry. Inclusion and/or use of the correspondingdata key type traits field 406 is optional.

The leaf node source name field 394 may include a leaf node source nameentry (e.g., leaf node distributed storage and task network (DSTN)address and/or a dispersed storage network (DSN) address 386)corresponding to a storage location of the leaf node. The leaf noderevision field 396 may include a leaf node revision entry correspondingto a revision number of information contained in the leaf node. Use ofthe leaf node revision enables generating two or more similar indexeswhile saving each revision of the two or more similar indexes. The nodetype field 366 includes a node type, where the node type indicateswhether the node is a leaf node or not a leaf node. The node typeindicates that the node is a leaf node when the node is the leaf node.

The sibling node source name field 368 includes a sibling node sourcename entry (e.g., sibling node DSN address) corresponding to a storagelocation for a sibling when the leaf node has the sibling node as asibling. The sibling node is another leaf node when the leaf node hasthe sibling. The sibling node source name field 368 may include a nullentry when the leaf node does not have a sibling. The sibling minimumindex key field 370 includes a minimum index key associated with thesibling node when the leaf node has the sibling node as the sibling. Thesibling key type traits field 372 may include sibling key type traitscorresponding to the sibling node when the leaf node has the siblingnode as the sibling and when the sibling key type traits field 372 isutilized. Alternatively, index structure metadata may include key typetraits utilized globally for each leaf node of the index structure.

The data source name or data field 402 includes at least one of a datasource name entry (e.g., a DSN address) corresponding to a storagelocation of data and the data (e.g., a data object, one or more encodeddata slices of data). For example, a data 1 source name or data field402 of a data 1 information section 398 includes a DSN address sourcename of a first data object. As another example, the data 1 source nameor data field 402 of the data 1 information section includes the data 1data object. The data index key field 404 includes a data index keycorresponding to the data. For example, a data 1 index key field orderfor the data 1 information section 398 includes a data 1 index key. Thedata key type traits field 406 may include data key type traitscorresponding to the data when the data key type traits field 406 isutilized. Alternatively, the index structure metadata may include keytype traits utilized globally for each data object associated with theindex structure.

FIG. 40D is a diagram illustrating another example of an index structureof an example index utilized to access data stored in at least one of adispersed storage network (DSN) memory and a distributed storage andtask network (DSTN) module. In the example, the index structure includesthree leaf nodes and three index nodes. Each of the three leaf nodes andthe three index nodes are individually encoded using a dispersed storageerror coding function to produce a set of corresponding node slices thatare stored in the DSTN module. The index structure provides an index forthree data objects stored in the DSTN module, where the data objectsstored in the DSTN module utilizing source names 76B, 8F6, and 92D, andglobal key type traits includes a comparing function to sort string typeindex keys alphabetically. The data stored at source name 76B isassociated with an index key of “a” as that data begins with a character“a”. The data stored at source name 8F6 is associated with an index keyof “d” as that data begins with a character “d”. The data stored atsource name 92D is associated with an index key of “j” as that databegins with a character “j”.

A leaf node stored at source name 5AB includes a node type indicating aleaf node, a sibling node source name pointing to a leaf node stored atsource name 52D, a sibling minimum index key of “d”, a data 1 sourcename of 76B, a data 1 index key of “a”, a data 2 direct data entry(e.g., b39d5ac9), and a data 2 index key of “b”. The leaf node stored atsource name 52D includes a node type indicating a leaf node, a siblingnode source name pointing to a leaf node stored at source name 539, asibling minimum index key of “j”, a data 1 source name of 8F6, and adata 1 index key of “d”. The leaf node stored at source name 539includes a node type indicating a leaf node, a null sibling node sourcename (e.g., since last leaf node of leaf node level), a null siblingminimum index key, a data 1 source name of 92D, and a data 1 index keyof “j”.

An index node stored at source name 4F7 includes a node type indicatingnot a leaf node (e.g., index node), a sibling node source name pointingto an index node stored at source name 42C, a sibling minimum index keyof “j”, a child 1 source name of 5AB, a child 1 minimum index key of“a”, a child 2 source name of 52D, and a child 2 minimum index key of“d”. The index node stored at source name 42C includes a node typeindicating not a leaf node (e.g., index node), a null sibling nodesource name (e.g., since last index node of an index node level), a nullsibling minimum index key, a child 1 source name of 539, and a child 1minimum index key of “j”. An index node (e.g., a root node) stored atsource name 2FD includes a node type indicating not a leaf node (e.g.,index node), a null sibling node source name (e.g., since root node), anull sibling minimum index key, a child 1 source name of 4F7, a child 1minimum index key of “a”, a child 2 source name of 42C, and a child 2minimum index key of “j”. An example of utilizing the index inaccordance with the index structure to access data is discussed withreference to FIG. 40E.

FIG. 40E is a flowchart illustrating an example of searching an indexstructure. The method begins at step 410 where a processing module (e.g.of a distributed storage and task (DST) client module) receives a searchindex request that includes a search value (e.g., from a distributedstorage and task network (DSTN) module). The request includes one ormore of a search value, a data object identifier (ID), a comparingfunction, comparing criteria, search criteria, a pathname, an object ID,an index name, an index ID, an index source name, a root node sourcename, and a user ID.

The method continues at step 412 where the processing module accesses aroot node as a current node. For example, the processing module extractsa root node source name from an index metadata lookup and retrieves theroot node from the DSTN module utilizing the root node source name. Themethod continues at step 414 where the processing module identifies achild node that is associated with a minimum index key that is thegreatest minimum index key of all child nodes of the current node thatare associated with a minimum index key less than or equal to the searchvalue. For example, the processing module identifies all the child nodesof the current node, retrieves a minimum index key associated with eachof the child nodes, identifies index keys that are less than the searchvalue as candidate index keys in accordance with the comparing function,identifies the index key of the candidate index keys that has thegreatest value in accordance with the comparing function, and identifiesthe child node associated with the index key of greatest value.

The method continues at step 416 where the processing module accessesthe child node as the current node (e.g., retrieves the child node andsets the child node as the current node). The method continues at step418 where the processing module determines whether the current node is aleaf node. The determination may be based on extracting a node type ofthe current node and comparing the node type to a value associated withleaf node. The method branches back to step 414 when the processingmodule determines that the current node is not a leaf node. The methodcontinues to step 420 when the processing module determines that thecurrent node is a leaf node.

The method continues at step 420 where the processing module determineswhether data of the current node matches the search value. For example,the processing module compares the search value to the data of thecurrent node. The method branches to step 424 when the processing moduledetermines that the data matches the search value. The method continuesto step 422 when the processing module determines that the data does notmatch the search value. The method continues at step 422 where theprocessing module outputs an indication that the data was not found(e.g., search failure). For example, the processing module sends theindication to a requesting entity.

The method continues at step 424 where the processing module extracts asource name of a data object matching the search value from the currentnode. Alternatively, the processing module extracts the data object fromthe current node when data is stored in the current node rather than asource name of the data object. The method continues at step 426 wherethe processing module outputs the source name (e.g., to the requestingentity). The method continues at step 428 where the processing moduleoutputs an indication that the data was found (e.g., to the requestingentity).

FIG. 41A is a diagram illustrating another example of an index structurethat includes a representation of a portion of a dispersed index thatincludes an index node 430 and a plurality of leaf nodes 432, 434, 436,and 438. As illustrated, each of the plurality of leaf nodes 432-438 arechild nodes with respect to the index node 430. As illustrated, leafnode 434 is a sibling node to leaf node 432, leaf node 436 as a siblingnode to leaf node 434, and leaf node 438 is a sibling node to leaf node436.

The plurality of leaf nodes 432-438 includes a corresponding pluralityof data object index keys that is ordered in accordance with ordering ofattributes of an attribute category where a data object index key of theplurality of data object index keys uniquely identifies one of aplurality of data objects in accordance with the attribute category. Forexample, the plurality of leaf nodes 432-438 includes a plurality ofdata object index keys that includes names of a portion of a phonebookwhere the plurality of object keys are ordered in accordance with analphabetical ordering of an alphabetical attribute category. Forinstance, leaf node 432 includes index keys for phonebook names A. Smiththrough E. Smith, leaf node 434 includes index keys for phonebook namesF. Smith through K. Smith, leaf node 436 includes index keys forphonebook names L. Smith through Q. Smith, and leaf node 438 includesindex keys for phonebook names T. Smith through A. Tait. The data objectindex key identifies the one of the plurality of data objects by anassociated dispersed storage network (DSN) address that corresponds to astorage location for the one of the plurality of data objects within aDSN.

The dispersed index enables generation of a data index list thatidentifies data objects having one or more common attributes of anattribute category where indexing of the plurality of data objects isorganized in accordance with the ordering of attributes of the attributecategory. For example, generation of a data index list includesidentifying data objects associated with data object index keys G.Smith, H. Smith, K. Smith, L. Smith, and M. Smith when the one or morecommon attributes includes identifying data objects associated with dataobject index keys starting with G. Smith and ending with M. Smith andthe attribute category includes alphabetized names. As another example,generation of a data index list includes identifying data objectsassociated with data object index keys Q. Smith, T. Smith, V. Smith, W.Smith, and A. Tait when the one or more common attributes includesidentifying data objects associated with data object index keys startingwith Q. Smith and higher (e.g., in an ascending alphabetized ordering)and the attribute category includes alphabetized names. As yet anotherexample, generation of a data index list includes identifying dataobjects associated with data object index keys F. Smith, E. Smith, D.Smith, B. Smith, and A. Smith when the one or more common attributesincludes identifying data objects associated with data object index keysstarting with F. Smith and lower (e.g., in a descending alphabetizedordering) and the attribute category includes alphabetized names.

In an example of operation, a request is received for a data index listthat identifies data objects having one or more common attributes (e.g.,G. Smith through M. Smith) of an attribute category (e.g., alphabetizednames). The hierarchical ordered index structure that maps the indexingof the plurality of data objects is searched to identify data objectlevel index node 434 of the index structure that includes a firstboundary data object index key (e.g., G. Smith) corresponding to a firstdata object boundary match of the one or more common attributes. Adetermination is made whether the data object level index node 434includes a second boundary data object index key (e.g., M. Smith)corresponding to a second data object boundary match of the one or morecommon attributes. Since the data object level index node 434 does notinclude the second boundary data object index key M. Smith, an adjacentdata object level index node 436 of the hierarchical ordered indexstructure is searched to determine whether the adjacent data objectlevel index node 436 includes the second boundary data object index keyM. Smith. Since the adjacent data object level index node 436 includesthe second boundary data object index key M. Smith, the data index listis generated to include the first boundary data object index key G.Smith, a first ordered set of data object index keys of the data objectlevel index node (e.g., H. Smith, K. Smith), the second boundary dataobject index key M. Smith, and a second ordered set of data object indexkeys (L. Smith) of the adjacent data object level index node 436. Next,the data index list is outputted to a requesting entity. The method togenerate the data index list is discussed in greater detail withreference to FIGS. 41B and 41C.

FIG. 41B is a schematic block diagram of an embodiment of a dispersedstorage system that includes a computing device 440 and a dispersedstorage network (DSN) 442. The DSN 442 may be implemented utilizing oneor more of multiple computers, multiple computing devices, a DSN memory,a distributed storage and task network (DSTN), and a DSTN module. TheDSN 442 includes a plurality of dispersed storage (DS) units 444. EachDS unit 444 of the plurality of DS units 444 may be implementedutilizing at least one of a storage server, a storage unit, a storagemodule, a memory device, a memory, a distributed storage and task (DST)execution unit, a user device, a DST processing unit, and a DSTprocessing module. The computing device 440 may be implemented utilizingat least one of a server, a storage unit, a storage server, a storagemodule, a DS processing unit, a DS unit, a DST execution unit, a userdevice, a DST processing unit, and a DST processing module. Thecomputing device 440 includes a dispersed storage (DS) module 446. TheDS module 446 includes a receive module 448, a search module 450, a listmodule 452, and a retrieve module 454.

The system functions to provide a data index list 456 that identifiesdata objects having one or more common attributes of an attributecategory where indexing of a plurality of data objects is organized inaccordance with an ordering of attributes of the attribute category andwhere the plurality of data objects is stored in the DSN 442. Theindexing of the plurality of data objects includes a correspondingplurality of data object index keys that is ordered in accordance withthe ordering of the attributes of the attribute category where a dataobject index key of the plurality of data object index keys uniquelyidentifies one of the plurality of data objects in accordance with theattribute category.

The providing of the data index list 456 includes one or more ofreceiving a request 458 for the data index list, searching ahierarchical ordered index structure, generating the data index list456, and retrieving a data object identified in the data index list 456.With regards to receiving the request 458, the receive module 448receives the request 458 for the data index list that identifies thedata objects having one or more common attributes of the attributecategory. The request 458 may include one or more of a dispersed indexidentifier (ID), the attribute category, the one or more commonattributes, one or more attribute traits, a search start value, a searchend value, a search range, a null search end indicator, an ascendingsearch ordering indicator, a descending search ordering indicator, adata object ID, a comparing function, comparing criteria, searchcriteria, a pathname, an object ID, an index name, an index ID, an indexsource name, a root node source name, a requesting entity ID, and a userID.

With regards to searching the hierarchical ordered index structure, thesearch module 450 performs a series of search steps. In a first searchstep, the search module 450 searches, based on the one or more commonattributes of the request 458, the hierarchical ordered index structurethat maps the indexing of the plurality of data objects to identify adata object level index node (e.g., a leaf node) of the index structurethat includes a first boundary data object index key (e.g., a startvalue) corresponding to a first data object boundary match of the one ormore common attributes. The search module 450 further functions toidentify the data object boundary match of the one or more commonattributes by identifying a first one of a plurality of index keysassociated with the data object level index node that includes anindication that substantially matches the one or more common attributes,where the plurality of index keys are sequentially ordered in accordancewith indicators corresponding to attributes of the attribute category.

The search module 450 searches the hierarchical ordered index structureby a sequence of sub-search steps. A first sub-search step includes thesearch module 450 identifying a category index node (e.g., an index nodeof the index structure) based on the request 458. For example, thesearch module 450 performs a dispersed index DSN address lookup based onthe attribute category of the request 458. A second sub-search stepincludes the search module 450 retrieving a set (e.g., at least a decodethreshold number) of category index slices 460 from the DSN 442. Forexample, the search module 450 issues slice requests 462 to the DSN 442based on the dispersed index DSN address and the one or more commonattributes. A third sub-search step includes the search module 450reconstructing a category index file from the set of category indexslices 460. For example, the search module 450 decodes the set ofcategory index slices 460 using a dispersed storage error codingfunction to reproduce the category index file. A fourth sub-search stepincludes the search module 450 interpreting the category index filebased on the one or more common attributes to identify a next levelindex node. When the next level index node is not the data object levelindex node (e.g., does not include a leaf node type indicator, does notinclude data object index keys), a loop is entered that includes aseries of further sub-search steps.

A first further sub-search step includes the search module 450retrieving a set (e.g., at least a decode threshold number) of nextlevel index slices 460 from the DSN 442. For example, the search module450 issues a set of next level index slice requests 462 to the DSN 442based on a DSN address of a next index file extracted from the nextlevel index node. A second further sub-search step includes the searchmodule 450 reconstructing the next index file from the set of next levelindex slices 460 (e.g., decoding at least a decode threshold number ofthe set of next level index slices 460 to reproduce the next indexfile). A third further sub-search step includes the search module 450interpreting the next index file based on the one or more commonattributes to identify a new next level index node. A fourth furthersub-search step includes the search module 450 determining whether thenew next level index node is the data object index node. When the newnext level index node is the data object index node, the loop is exited.When the new next level index node is not the data object index node,the loop is repeated with the new next level index node as the nextlevel index node.

In a second search step to search the hierarchical ordered indexstructure, the search module 450 determines whether the data objectlevel index node includes a second boundary data object index key (e.g.,an end value) corresponding to a second data object boundary match ofthe one or more common attributes. When the data object level index nodedoes not include the second boundary data object index key, in a thirdsearch step, the search module 450 searches an adjacent data objectlevel index node (e.g., a sibling node) of the hierarchical orderedindex structure to determine whether the adjacent data object levelindex node includes the second boundary data object index key. Forexample, the search module 450 issues slice requests 462 to the DSN 442based on a DSN address of the adjacent data object level index nodeextracted from the data object level index node when the adjacent dataobject level index node is a sibling node to the data object level indexnode. As another example, the search module 450 issues slice requests462 to the DSN 442 based on the DSN address of the adjacent data objectlevel index node extracted from a parent node to the data object levelindex node. For instance, the search module 450 extracts the DSN addressassociated with a next child node in a descending order from the dataobject level index node.

When the adjacent data object level index node does not include thesecond boundary data object index key, a loop is entered to include aseries of alternative search steps. In a first alternative search step,the search module 450 searches a next adjacent data object level indexnode of the hierarchical ordered index structure to determine whetherthe next adjacent data object level index node includes the secondboundary data object index key. When the next adjacent data object levelindex node includes the second boundary data object index key, the loopis exited and a second alternative search step includes the searchmodule 450 generating the data index list 456. When the next adjacentdata object level index node does not include the second boundary dataobject index key, the loop is repeated using a second next adjacent dataobject level index node as the next adjacent data object level indexnode. For example, the search module 450 identifies a sibling node ofthe sibling node of the data object level index node.

With regards to generating the data index list 456, the list module 452performs a series of listing steps. In a first listing step, when theadjacent data object level index node includes the second boundary dataobject index key, the list module 452 generates the data index list 456to include the first boundary data object index key, a first ordered setof data object index keys of the data object level index node, thesecond boundary data object index key, and a second ordered set of dataobject index keys of the adjacent data object level index node. The listmodule 452 generates the data index list by determining DSN addressescorresponding to each of the data object index keys in the data indexlist 456 and adding the DSN addresses to the data index list 456. Whenthe data object level index node includes the second boundary dataobject index key, the list module 452, in the first listing step,generates the data index list 456 to include the first boundary dataobject index key, the second boundary data object index key, and anordered set of data object index keys between the first and secondboundary data object index keys. In a second listing step, the listmodule 452 outputs the data index list 456.

With regards to retrieving the data object identified in the data indexlist 456, the retrieve module 454 performs a series of retrieving steps.In a first retrieving step, the retrieve module 454 receives a readrequest 464 regarding a data object identified in the data index list456. In a second retrieving step, the retrieve module 454 determines aDSN address for the data object. In a third retrieving step, theretrieve module 454 identifies a set of DS servers (e.g., DS units 444)based on the DSN address (e.g., a lookup). In a fourth retrieving step,the retrieve module 454 sends a set of read requests 466 to the set ofDS servers 444 to retrieve a set of data slices 468, where a datasegment of the data object is stored as the set of data slices 468 inthe set of DS servers 444. In a fifth retrieving step, the retrievemodule 454 decodes the set of data slices 468 to reproduce the datasegment. In a sixth retrieving step, the retrieve module 454 outputs aread response 470 that includes the data segment. In addition, theretrieve module 454 may decode a plurality of sets of data slices 468 togenerate a plurality of data segments of the data object for inclusionin the read response 470.

FIG. 41C is a flowchart illustrating an example of listing an indexstructure. The method begins at step 472 where a processing module(e.g., of a dispersed storage (DS) processing module of a computer)receives a request for a data index list that identifies data objectshaving one or more common attributes of an attribute category, whereindexing of a plurality of data objects is organized in accordance withan ordering of attributes of the attribute category and where theplurality of data objects is stored in a dispersed storage network(DSN). The indexing of the plurality of data objects includes acorresponding plurality of data object index keys that is ordered inaccordance with the ordering of the attributes of the attributecategory, where a data object index key of the plurality of data objectindex keys uniquely identifies one of the plurality of data objects inaccordance with the attribute category.

The method continues at step 474 where the processing module searches,based on the one or more common attributes, a hierarchical ordered indexstructure that maps the indexing of the plurality of data objects toidentify a data object level index node of the index structure thatincludes a first boundary data object index key corresponding to a firstdata object boundary match of the one or more common attributes. Theidentifying the data object boundary match of the one or more commonattributes includes identifying a first one of a plurality of index keysassociated with the data object level index node that includes anindication that substantially matches the one or more common attributes,where the plurality of index keys are sequentially ordered in accordancewith indicators corresponding to attributes of the attribute category.

The searching the hierarchical ordered index structure includes a seriesof steps. A first step includes identifying a category index node basedon the request. A second step includes retrieving a set of categoryindex slices from the DSN. A third step includes reconstructing acategory index file from the set of category index slices. A fourth stepincludes interpreting the category index file based on the one or morecommon attributes to identify a next level index node. When the nextlevel index node is not the data object level index node, a fifth stepincludes entering a loop that includes a series of steps. A first loopstep includes retrieving a set of next level index slices from the DSN.A second loop step includes reconstructing a next index file from theset of next level index slices. A third loop step includes interpretingthe next index file based on the one or more common attributes toidentify a new next level index node. A fourth loop step includesdetermining whether the new next level index node is the data objectindex node. When the new next level index node is the data object indexnode, a sixth step includes exiting the loop. When the new next levelindex node is not the data object index node, the sixth step includesrepeating the loop with the new next level index node as the next levelindex node.

The method continues at step 476 where the processing module determineswhether the data object level index node includes a second boundary dataobject index key corresponding to a second data object boundary match ofthe one or more common attributes. The method branches to step 480 whenthe data object level index node includes the second boundary dataobject index key corresponding to the second data boundary match of theone or more common attributes. The method continues to step 478 when thedata object level index node does not include the second boundary dataobject index key corresponding to the second data boundary match of theone or more common attributes.

When the data object level index node does not include the secondboundary data object index key, the method continues at step 478 wherethe processing module searches an adjacent data object level index nodeof the hierarchical ordered index structure to determine whether theadjacent data object level index node includes the second boundary dataobject index key. When the adjacent data object level index node doesnot include the second boundary data object index key, the processingmodule functions to perform another set of loop steps. A first otherloop step includes searching a next adjacent data object level indexnode of the hierarchical ordered index structure to determine whetherthe next adjacent data object level index node includes the secondboundary data object index key. When the next adjacent data object levelindex node includes the second boundary data object index key, a secondother loop step includes exiting the loop and branching to step 480 togenerate the data index list. When the next adjacent data object levelindex node does not include the second boundary data object index key,the second other loop step includes repeating the loop using a secondnext adjacent data object level index node as the next adjacent dataobject level index node.

When the adjacent data object level index node includes the secondboundary data object index key, the method continues at step 480 wherethe processing module generates the data index list to include the firstboundary data object index key, a first ordered set of data object indexkeys of the data object level index node, the second boundary dataobject index key, and a second ordered set of data object index keys ofthe adjacent data object level index node. The generating the data indexlist includes determining DSN addresses corresponding to each of thedata object index keys in the data index list and adding the DSNaddresses to the data index list. Alternatively, when the data objectlevel index node includes the second boundary data object index key,generating, by the processing module, the data index list to include thefirst boundary data object index key, the second boundary data objectindex key, and an ordered set of data object index keys between thefirst and second boundary data object index keys. The method continuesat step 482 where the processing module outputs the data index list.

The data index list may be utilized to identify and retrieve a dataobject of the plurality of data objects. When utilizing the data indexlist, the method continues at step 484 where the processing modulereceives a read request regarding the data object identified in the dataindex list. The method continues at step 486 where the processing moduledetermines a DSN address for the data object. The method continues atstep 488 where the processing module identifies a set of dispersedstorage (DS) servers based on the DSN address. The method continues atstep 490 where the processing module sends a set of read requests to theset of DS servers to retrieve a set of data slices, where a data segmentof the data object is stored as the set of data slices in the set of DSservers.

FIG. 42A is a diagram illustrating an example of an index metadatastructure of a plurality of index metadata structures 1-S. Each indexmetadata structure describes a corresponding dispersed index utilized toaccess data stored in a distributed storage and task network (DSTN)and/or a dispersed storage network (DSN). The index metadata structure 1includes one or more of an index name field 502, an index root nodesource name field 504, a maximum spans field 506, a maximum layers field508, and trait definitions 500. The index name field 502 may include oneor more entries including one or more of an index identifier (ID) of acorresponding index, an index owner ID, and a naming convention. Theindex root node source name field 504 includes a source name entrycorresponding to storage of a root node of the index. The maximum spansfield 506 includes a maximum spans entry indicating how many spans ofthe index structure are allowed (e.g., a maximum number of nodes of acommon row of the index structure). The maximum layers field 508includes a maximum layers entry indicating how many layers of the indexstructure are allowed (e.g., a maximum number of nodes deep of the indexstructure).

The trait definitions 500 includes one or more of an index key typefield 510, a vault name field 512, a vault ID field 514, an index keyminimum value field 516, a comparison function field 518, and acomparison function ID field 520. The index key type field 510 includesa index key type entry indicating a general type of the index which maybe utilized for searching for data indexed by the index structure (e.g.,by alphanumeric, by file names, etc.). The vault name field 512 includesone or more vault names, where the index indexes data stored in the DSTNthat is associated with the one or more vault names. The vault ID field514 includes one or more vault IDs, where the index indexes data storedin the DSTN that is associated with the one or more vault IDs. The indexkey minimum value field 516 includes an index key minimum value entrycorresponding to a lowest allowed index key value of data indexed by theindex. The comparison function field 518 includes one or more comparisonfunction entries, where each entry identifies a type of comparisonfunction that may be utilized to search data associated with the index.The comparison function may include at least one of a compare magnitudeof integers, a compare dates function, a compare lexically storedstrings function, a compare strings with case sensitivity function, acompare strings without case sensitivity function, and a sort by thelimiter function. The comparison function ID field 520 includes one ormore comparison function ID entries, wherein the comparison function IDentry references a corresponding comparison function.

Two or more of the index metadata structures may be utilized to accesscommon data. One or more of the index metadata structures of theplurality of index metadata structures 1-S may be selected and utilizedto search DSTN data for data that corresponds to one or more searchvalues. A method to select an index metadata structure for utilizationin a search scenario is described in greater detail with reference toFIG. 42B.

FIG. 42B is a flowchart illustrating an example of identifying an index.The method begins at step 522 where a processing module (e.g. of adistributed storage and task (DST) client module) receives a data accessrequest. The request may include one or more of an index key type, anindex identifier (ID), an index name, a comparison function ID, acomparison function, and one or more search values. The method continuesat step 524 where the processing module identifies an index based on therequest. The identifying includes comparing the request to one or moretrait definitions of one or more index metadata structures. For example,processing module identifies the index when an index key type of therequest substantially matches an index key type of trait definitions ofthe identified index metadata structure.

The method continues at step 526 where the processing module accessesindex metadata corresponding to the identified index. For example, theprocessing module retrieves the index metadata from a distributedstorage and task network (DSTN) module. The method continues at step 528where the processing module accesses the index associated with the indexmetadata. For example, the processing module retrieves an index rootnode source name from the index metadata and accesses a root node storedin the DSTN module utilizing the index root node source name. The methodcontinues at step 530 where the processing module accesses data of theDSTN module utilizing the index. For example, the processing modulesearches a corresponding index utilizing the root node to identify datacorresponding to a search value. Alternatively, the processing modulemay identify a second index based on the request when a data accessfailure occurs utilizing the index.

FIG. 43A is a schematic block diagram of another embodiment of adispersed storage system that includes a computing device 532 and adispersed storage network (DSN) 534. The DSN 534 may be implementedutilizing one or more of multiple computers, multiple computing devices,a DSN memory, a distributed storage and task network (DSTN), and a DSTNmodule. The DSN 534 includes a plurality of dispersed storage (DS) units536. The plurality of DS units 536 includes at least one set of DS units536. Each DS unit 536 of the plurality of DS units 536 may beimplemented utilizing at least one of a storage server, a storage unit,a storage module, a memory device, a memory, a distributed storage andtask (DST) execution unit, a user device, a DST processing unit, and aDST processing module. The computing device 532 may be implementedutilizing at least one of a server, a storage unit, a storage server, astorage module, a DS processing unit, a DS unit, a DST execution unit, auser device, a DST processing unit, and a DST processing module. Thecomputing device 532 includes a dispersed storage (DS) module 538. TheDS module 538 includes a determine updating module 540 and an updatemodule 542.

The system functions to determine to create or modify a data objectindex key and to update a data object level index node that stores thedata object index key where a hierarchical ordered index structureincludes the data object level index node. The hierarchical orderedindex structure includes a plurality of data object index keys that isordered in accordance with the ordering of the attributes of the firstattribute category, where a data object index key of the plurality ofdata object index keys uniquely identifies one of a plurality of dataobjects stored in the DSN 534 in accordance with the first attributecategory.

With regards to creating or modifying the data object index key, thedetermine updating module 540 determines to create or modify the dataobject index key of a data object regarding an attribute 544 of a firstattribute category of a plurality of attribute categories. The determineupdating module 540 determines to create the data object index key byreceiving a request 546 to write the data object into the DSN 534,wherein the request includes the attribute 544. Alternatively, therequest 546 includes a plurality of attributes of the data object, wherethe plurality of attributes includes the attribute 544, and where otherattributes of the plurality of attributes are from different attributecategories of the plurality of attribute categories. For each of theother attributes of the plurality of attributes, the determine updatingmodule 540 creates a corresponding data object index key for the dataobject regarding the other attribute and facilitates entering a processto update the data object level index node, where a corresponding dataobject level index node associated with the other attributes is the dataobject level index node to be updated.

The determine updating module 540 determines to modify the data objectindex key by one of deleting the data object index key and entering theprocess to update the data object level index node by deleting the dataobject index key and changing the attribute 544 to a different attributeof the first attribute category and entering the process to update thedata object level index node by modifying the data object index key. Thedetermine updating module 540 changes the attribute 544 by a series ofchange steps. A first change step includes the determine updating module540 determining to create a new data object index key in another dataobject level index node regarding the different attribute. For example adata object has a first priority and is being changed to a secondpriority (e.g., higher or lower). A second change step includes thedetermine updating module 540 determining to delete the data objectindex key in the data object level index node. A third change stepincludes the determine updating module 540 entering the process toupdate the data object level index node by deleting the data objectindex key. A fourth change step includes the determine updating module540 facilitating entering the process to update the other data objectlevel index node by adding the new data object index key.

With regards to updating the data object level index node, the updatemodule 542 enters the process to update the data object level index nodeusing a series of updating steps. In a first updating step, the updatemodule 542 accesses a hierarchical ordered index structure associatedwith the first attribute category to identify the data object levelindex node containing data object index keys associated with theattribute. The update module 542 accesses the hierarchical ordered indexstructure by a series of accessing steps. A first accessing stepincludes the update module 542 identifying a category index node basedon the first attribute category (e.g., a DSN address lookup for thecategory index node based on the first attribute category). A secondaccessing step includes the update module 542 retrieving a set ofcategory index slices (e.g., at least a decode threshold number) fromthe DSN 534 by receiving a set of slice responses 548 that includes theset of category index slices. The retrieving further includes issuing aset of slice requests 550 based on the DSN address of the category indexnode. A third accessing step includes the update module 542reconstructing a category index node from the set of category indexslices (e.g., decoding the set of category index slices using adispersed storage error coding function to reconstruct the categoryindex node). A fourth accessing step includes the update module 542interpreting the category index node based on the one or more searchattributes to identify a next level index node. The interpretingincludes selecting the next level index node that is associated with aminimum data object index key that is less than or equal to the one ormore search attributes and a sibling node (e.g., an adjacent node to theright, if any) to the next level index node is associated with a minimumdata object index key that is greater than the one or more searchattributes.

A fifth accessing step to access the hierarchical ordered indexstructure includes the update module 542 retrieving a set of next levelindex slices from the DSN (e.g., retrieve at least a decode thresholdnumber of next level index slices from a set of slice responses 548 inresponse to a set of slice requests 550 based on a DSN address of theidentified next level index node). A sixth accessing step includes theupdate module 542 reconstructing the next level index node from the setof next level index slices (e.g., decode the set of next level indexslices to reconstruct the next level index node exposing a node typeindicator (i.e. a node data object level index node)). When the nextlevel index node is not the data object level index node (e.g., not aleaf node based on a node type indicator), a seventh step includes theupdate module 542 entering a loop that includes a series of loop steps.A first loop step includes the update module 542 interpreting the nextlevel index node based on the attribute to identify a new next levelindex node. A second loop step includes the update module 542 retrievinga set of new next level index slices from the DSN (e.g., retrieving atleast a decode threshold number of new next level index slices fromslice responses 548 in response to a set of slice requests 550 based ona DSN address of the identified new next level index node). A third loopstep includes the update module 542 reconstructing the new next levelindex node from the set of new next level index slices (e.g., decode). Afourth loop step includes the update module 542 determining whether thenew next level index node is the data object level index node (e.g.,extract a node type indicator to determine if it is a leaf node). Whenthe new next level index node is the data object level index node, afifth loop step includes the update module 542 exiting the loop. Whenthe new next level index node is not the data object level index node,the fifth loop step includes the update module 542 repeating the loopwith the new next level index node as the next level index node.

In a second updating step to update the data object level index node,the update module 542 retrieves the data object level index node from aset of dispersed storage (DS) units of the DSN. For example, the updatemodule 542 issues a set of slice requests 550 based on a DSN address ofthe data object level index node, receives at least a decode thresholdnumber of slice responses 548 that includes an update reference value,(i.e. a revision number for at least a decode threshold number of indexnode slices), and decodes the at least the decode threshold number ofslice responses 548 to reproduce the data object level index node. In athird updating step, the update module 542 adds or modifies the dataobject index key in the data object level index node in accordance withan ordering of attributes of the attribute category to produce anupdated data object level index node. The update module 542 adds thedata object index key in the data object level index node by a series ofadding steps. A first adding step includes the update module 542appending the data object index key to the data object level index nodeto produce an unsorted data object level index node (e.g., extract thedata object index key from the request 546, add a DSN address of thedata object, and add the index key associated with the data object). Asecond adding step includes the update module 542 sorting a plurality ofdata object index keys of the unsorted data object level index node inaccordance with an ordering of attributes of the attribute category toproduce an updated data object level index node to produce the updateddata object level index node (e.g., sort the data object entry using theone or more search attributes).

In a fourth updating step to update the data object level index node,the update module 542 encodes the updated data object level index nodein accordance with a dispersed storage error coding function to producea set of encoded index node slices. In a fifth updating step, the updatemodule 542 generates a set of write commands (e.g., slice requests 550)to write the set of encoded index node slices to the set of DS units536, wherein each of the set of write commands includes an updatereference value that is based on a reference value of the data objectlevel index node. For example, the update module 542 extracts a revisionnumber from one or more read slice responses 548 associated withretrieving slices of the data object level index node as the referencevalue of the data object level index node and establishing the updatereference value as an expected revision number to be substantially thesame as the extracted revision number. As another example, thegenerating further includes the generating the set of write commands 550to include one or more of the set of encoded index node slices, achecked write operation code, the expected revision number, and a newrevision number that is the expected revision number incremented by one.

When at least a threshold number of the set of write commands 550 aresuccessfully executed by the set of DS units 536, in a sixth updatingstep, the update module 542 exits the process to update the data objectlevel index node. The update module 542 determines whether the at leastthe threshold number of the set of write commands 550 are successfullyexecuted by the set of DS units 536 by receiving a status codes (e.g.,included in slice responses 548) from the set of DS units 536 indicatingwhether there is a checked write error due to a revision mismatch andindicating successful execution when at least a write threshold numberof status codes indicate that there is no mismatch.

When less than the threshold number of the set of write commands aresuccessfully executed by the set of DS units, in the sixth updatingstep, the update module 542 repeats the process to update the dataobject level index node. For example, the update module 542 determinesto start over since another process modified the data object level indexnode since reading the data object level index node and the processshould modify a most recent revision of the data object level indexnode. Alternatively, the update module 542 interprets that anothercomputer of the DSN 534 has priority in updating the data object levelindex node when the less than the threshold number of the set of writecommands are successfully executed by the set of DS units 536 and (e.g.,actually assigned or priority in time) repeats the process to update thedata object level index node in accordance with a priority scheme. Thepriority scheme includes at least one of waiting a predetermined amountof time, sending a priority interrupt message, and starting the processover.

FIG. 43B is a flowchart illustrating an example of modifying an index.The method begins at step 560 where a processing module (e.g., of adispersed storage (DS) processing module of a computing device)determines to create or modify a data object index key of a data objectregarding an attribute of a first attribute category of a plurality ofattribute categories. The determining to create the data object indexkey includes receiving a request to write the data object into adispersed storage network (DSN), where the request includes theattribute. Alternatively, the processing module may receive a requestwith regards to the data object index key for the data object that waspreviously stored in the DSN. The request may include a plurality ofattributes of the data object, where the plurality of attributesincludes the attribute, and where other attributes of the plurality ofattributes are from different attribute categories of the plurality ofattribute categories. For each of the other attributes of the pluralityof attributes, the processing module may facilitate creating acorresponding data object index key for the data object regarding theother attribute and facilitates entering a process at step 562 to updatea data object level index node, where a corresponding data object levelindex node associated with the other attributes is the data object levelindex node to be updated.

The determining to modify the data object index key includes one ofdeleting the data object index key and entering the process at step 562to update the data object level index node by deleting the data objectindex key and changing the attribute to a different attribute of thefirst attribute category and entering the process at step 562 to updatethe data object level index node by modifying the data object index key.The changing the attribute further includes several changing steps. Afirst changing step includes the processing module determining to createa new data object index key in another data object level index noderegarding the different attribute. A second changing step includes theprocessing module determining to delete the data object index key in thedata object level index node. A third changing step includes theprocessing module entering the process to update the data object levelindex node by deleting the data object index key. A fourth changing stepincludes the processing module entering the process to update the otherdata object level index node by adding the new data object index key.

The method continues at step 562 where the processing module facilitatesentering the process to update a data object level index node. Theprocess starts with the processing module accessing a hierarchicalordered index structure associated with the first attribute category toidentify the data object level index node containing data object indexkeys associated with the attribute. The hierarchical ordered indexstructure includes a plurality of data object index keys that is orderedin accordance with the ordering of the attributes of the first attributecategory, where a data object index key of the plurality of data objectindex keys uniquely identifies one of a plurality of data objects inaccordance with the first attribute category.

The accessing the hierarchical ordered index structure includes a seriesof accessing steps. A first accessing step includes identifying acategory index node based on the first attribute category (e.g., performa DSN address lookup based on the attribute category). A secondaccessing step includes retrieving a set of category index slices (e.g.,at least a decode threshold number) from the DSN. A third accessing stepincludes reconstructing (e.g., decoding slices) a category index nodefrom the set of category index slices. A fourth accessing step includesinterpreting the category index node based on the one or more searchattributes to identify a next level index node. The interpretingincludes selecting the next level index node that is associated with aminimum data object index key that is less than or equal to theattribute and a sibling node (e.g., an adjacent node to the right, ifany) to the next level index node that is associated with a minimum dataobject index key that is greater than the attribute. A fifth accessingstep includes retrieving a set of next level index slices from the DSN(e.g., retrieve based on a DSN address of the identified next levelindex node retrieving at least a decode threshold number of slices). Asixth accessing step includes reconstructing the next level index nodefrom the set of next level index slices (e.g., decoding slices to exposea node type indicator (index/leaf)).

When the next level index node is not the data object level index node(e.g., not a leaf node based on a leaf node indicator), the processingmodule facilitates entering a loop that includes a series of loop steps.A first loop step includes interpreting the next level index node basedon the attribute to identify a new next level index node. A second loopstep includes retrieving a set of new next level index slices from theDSN (e.g., retrieve slices from a DSN address of the identified new nextlevel index node retrieving at least a decode threshold number ofslices). A third loop step includes reconstructing (e.g., decode slices)the new next level index node from the set of new next level indexslices. A fourth loop step includes determining (e.g., extract a nodetype indicator to determine if it is a leaf node) whether the new nextlevel index node is the data object level index node. When the new nextlevel index node is the data object level index node, the processingmodule facilitates exiting the loop. When the new next level index nodeis not the data object level index node, the processing modulefacilitates repeating the loop with the new next level index node as thenext level index node.

The method continues at step 564 where the processing module retrievesthe data object level index node from a set of dispersed storage (DS)units of the DSN. For example, the processing module issues a set ofread slice requests based on the DSN address of the data object levelindex node, receives at least a decode threshold number of read sliceresponses that include an update reference value, i.e., a revisionnumber for at least a decode threshold number of index node slices, anddecodes the at least the decode threshold number of index node slices toreproduce the data object level index node.

The method continues at step 566 where the processing module adds ormodifies the data object index key in the data object level index nodein accordance with an ordering of attributes of the attribute categoryto produce an updated data object level index node. The adding the dataobject index key in the data object level index node includes a seriesof adding steps. A first adding step includes the processing moduleappending the data object index key to the data object level index nodeto produce an unsorted data object level index node. A second addingstep includes the processing module sorting (e.g., sort the data objectentry using the one or more search attributes) a plurality of dataobject index keys of the unsorted data object level index node inaccordance with an ordering of attributes of the attribute category toproduce an updated data object level index node to produce the updateddata object level index node.

The method continues at step 568 where the processing module and encodesthe updated data object level index node in accordance with a dispersedstorage error coding function to produce a set of encoded index nodeslices. The method continues at step 570 where the processing modulegenerates a set of write commands to write the set of encoded index nodeslices to the set of DS units, where each of the set of write commandsincludes an update reference value that is based on a reference value ofthe data object level index node. For example, the processing moduleextracts a revision number from one or more read slice responsesassociated with retrieving slices of the data object level index node asthe reference value of the data object level index node and establishesthe update reference value as an expected revision number to besubstantially the same as the extracted revision number. The generatingfurther includes the processing module generating the set of writecommands to include one or more of the set of encoded index node slices,a checked write operation code, the expected revision number, and a newrevision number that is the expected revision number incremented by one.

The method continues at step 572 where the processing module determineswhether at least a threshold number of the set of write commands aresuccessfully executed by the set of DS units. The determining includesthe processing module receiving status codes from the set of DS unitsindicating whether there is a checked write error due to a revisionmismatch and indicating successful execution when at least a thresholdnumber (e.g., a write threshold number) of status codes indicate thatthere is no mismatch. When the processing module determines that theless than the threshold number of the set of write commands are notsuccessfully executed by the set of DS units, the method loops back tostep 562 repeating the process to update the data object level indexnode. Alternatively, or in addition to, the processing module interpretsthat another computer of the DSN has priority in updating the dataobject level index node when the less than the threshold number of theset of write commands are not successfully executed by the set of DSunits. The processing module facilitates repeating the process, bybranching to step 562, to update the data object level index node inaccordance with a priority scheme when the processing module interpretsthat the other computer of the DSN has priority in updating the dataobject level index node. When the processing module determines that theat least the threshold number of the set of write commands aresuccessfully executed by the set of DS units, the method branches tostep 574. The method continues at step 574 where the processing modulefacilitates exiting the process to update the data object level indexnode when the processing module determines that the at least thethreshold number of the set of write commands are successfully executedby the set of DS units.

FIG. 44A is a diagram illustrating another example of an index structureprior to combining nodes. An index structure diagram representing theindex structure after combining nodes is represented in FIG. 44B. Theindex structure includes three nodes 576, 578, and 580 of an index. Thethree nodes includes an index node 576 stored in a distributed storageand task network (DSTN) at a source name address of 4F7 and two leafnodes 578 and 580 stored at source name addresses 5AB and 52D, whereinthe two leaf nodes are joined in the example as illustrated in FIG. 44B.

The index node includes a node type indicating not a leaf node (e.g.,index node), a sibling node source name pointing to an index node storedat source name 42C, a sibling minimum index key of “j”, a child 1 sourcename of 5AB, a child 1 minimum index key of “a”, a child 2 source nameof 52D, and a child 2 minimum index key of “d”. The leaf node stored atsource name 5AB includes a node type indicating a leaf node, a siblingnode source name pointing to the leaf node stored at source name 52D, asibling minimum index key of “d”, a data 1 source name of 76B, a data 1index key of “a”, a data 2 direct data entry (e.g., b39d5ac9), and adata 2 index key of “b”. The leaf node stored at source name 52Dincludes a node type indicating a leaf node, a sibling node source namepointing to a leaf node stored at source name 539, a sibling minimumindex key of “j”, a data 1 source name of 8F6, and a data 1 index key of“d”.

FIG. 44B is a diagram illustrating another example of an index structureafter joining two leaf nodes illustrated in FIG. 44A. An index structurediagram representing the index structure prior to joining nodes isrepresented in FIG. 44A. The index structure after the joining includestwo nodes 582 and 584 of an index. The two nodes includes an index node582 stored in a distributed storage and task network (DSTN) at a sourcename address of 4F7 and a new leaf node 584 stored at source nameaddress 65C, wherein the new leaf node includes entries of two leafnodes prior to the joining.

The new leaf node stored at source name 65C includes a node typeindicating a leaf node, a sibling node source name pointing to a leafnode stored at source name 539, a sibling minimum index key of “j”, adata 1 source name of 76B, a data 1 index key of “a”, a data 2 directdata entry (e.g., b39d5ac9), a data 2 index key of “b”, a data 3 sourcename of 8F6, and a data 3 index key of “d”.

FIG. 44C is a diagram illustrating an example of an index structure of astarting step of a series of example steps depicted in FIGS. 44D through44J that includes a representation of a portion of a dispersed indexthat includes a common parent index node 586 and a plurality of leafnodes 588, 590, 592, and 594. As illustrated, each of the plurality ofleaf nodes 588-594 are child nodes with respect to the index node 586.As illustrated, leaf node 594 is a sibling node to leaf node 592, leafnode 592 as a sibling node to leaf node 590, and leaf node 590 is asibling node to leaf node 588.

The plurality of leaf nodes 588-594 includes a corresponding pluralityof data object index keys that is ordered in accordance with ordering ofattributes of an attribute category where a data object index key of theplurality of data object index keys uniquely identifies one of aplurality of data objects in accordance with the attribute category. Forexample, the plurality of leaf nodes 588-594 includes a plurality ofdata object index keys that includes names of a portion of a phonebookwhere the plurality of object keys are ordered in accordance with analphabetical ordering of an alphabetical attribute category. Forinstance, leaf node 588 includes index keys for phonebook names A. Smithand B. Smith, leaf node 590 includes index keys for phonebook names F.Smith and G. Smith, leaf node 592 includes index keys for phonebooknames L. Smith and M. Smith, and leaf node 594 includes index keys forphonebook names T. Smith and V. Smith. The data object index keyidentifies the one of the plurality of data objects by an associateddispersed storage network (DSN) address that corresponds to a storagelocation for the one of the plurality of data objects within a DSN.

The dispersed index enables merging of two leaf nodes (e.g., two dataobject level index nodes) when a determination is made to merge the twoleaf nodes. The determination may be based on one or more of a number ofleaf nodes of a data object level, a number of data object index keysassociated with a leaf node, a request, a frequency of access level, anda performance level of the dispersed index. The series of example stepsdepicted in FIGS. 44D through 44J represent three examples of themerging. FIGS. 44D-E illustrate an example of the merging when leaf node588 is identified as one data object level index node of the two dataobject index level nodes. FIGS. 44F-G illustrate another example of themerging when leaf node 590 is identified as the one data object levelindex node of the two data object index level nodes. FIGS. 44H-Jillustrate another example of the merging when leaf node 592 or 594 isidentified as the one data object level index node of the two dataobject index level nodes. The method to merge the two leaf nodes isdiscussed in greater detail with reference to FIGS. 44D through 44L.

FIG. 44D is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J. FIGS. 44D-E illustrate an example of the merging when leafnode 588 is identified as one data object level index node of the twodata object index level nodes. In a first sub-step of the merging, dataobject level index node (e.g., leaf node) 588 is selected for merging.In a second sub-step of the merging, data object level index node 590 isidentified for merging with the selected data object level index node toproduce the two data object level index nodes. In a third sub-step ofthe merging, the two data object level index nodes are merged into atemporarily merged data object level index node 596 where thetemporarily merged data object level index node includes a sibling entry(e.g., sibling dispersed storage network (DSN) address and siblingminimum index key) that is identical to the sibling entry of data objectlevel node 590 and includes data object entries of the two data objectlevel index nodes. In a fourth sub-step of the merging, the temporarilymerged data object level index node 596 is stored in the DSN.

FIG. 44E is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J. FIGS. 44D-E illustrate an example of the merging when leafnode 588 is identified as one data object level index node of the twodata object index level nodes. In a fifth sub-step of the merging, thecommon parent index node 586 is updated to include overwriting adispersed storage network (DSN) address of a child entry associated withdata object level index node 588 with a DSN address of the temporarilymerged data object level index node 596 and to remove a child entryassociated with data object level index node 590. In a sixth sub-step ofthe merging, the two data object level index nodes 588-590 are deleted.When a change is detected of either the common parent index node 586 orthe two data object level index nodes 588-590 subsequent to the firstsub-step of the merging and prior to final confirmation of deletion ofthe two data object level index nodes 588-590, the merging includesfacilitating rolling back the updating of the common parent index node586 and the deletion of the two data object level index nodes 588-590.The facilitating may also include deletion of the temporarily mergeddata object level index node 596 from the DSN.

FIG. 44F is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J. FIGS. 44F-G illustrate another example of the merging whenleaf node 590 is identified as the one data object level index node ofthe two data object index level nodes. In a first sub-step of themerging, data object level index node (e.g., leaf node) 590 is selectedfor merging. In a second sub-step of the merging, data object levelindex node 592 is identified for merging with the selected data objectlevel index node to produce the two data object level index nodes. In athird sub-step of the merging, the two data object level index nodes aremerged into a temporarily merged data object level index node 588 wherethe temporarily merged data object level index node 588 includes asibling entry (e.g., sibling dispersed storage network (DSN) address andsibling minimum index key) that is identical to the sibling entry ofdata object level node 592 and includes data object entries of the twodata object level index nodes. In a fourth sub-step of the merging, thetemporarily merged data object level index node 598 is stored in theDSN.

FIG. 44G is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J. FIGS. 44F-G illustrate another example of the merging whenleaf node 590 is identified as the one data object level index node ofthe two data object index level nodes. In a fifth sub-step of themerging, the common parent index node 586 is updated to includeoverwriting a dispersed storage network (DSN) address of a child entryassociated with data object level index node 590 with a DSN address ofthe temporarily merged data object level index node and to remove achild entry associated with data object level index node 592. In a sixthsub-step of the merging, the two data object level index nodes 590-592are deleted. When a change is detected of either the common parent indexnode 586 or the two data object level index nodes 590-592 subsequent tothe first sub-step of the merging and prior to final confirmation ofdeletion of the two data object level index nodes 590-592, the mergingincludes facilitating rolling back the updating of the common parentindex node 586 and the deletion of the two data object level index nodes590-592. The facilitating may also include deletion of the temporarilymerged data object level index node 598 from the DSN.

FIG. 44H is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J. FIGS. 44H-J illustrate another example of the merging whenleaf node 592 or 594 is identified as the one data object level indexnode of the two data object index level nodes. In a first sub-step ofthe merging, data object level index node (e.g., leaf node) 582 or 594is selected for merging. In a second sub-step of the merging, dataobject level index node 594 is identified for merging with the selecteddata object level index node to produce the two data object level indexnodes when leaf node 592 is the node selected for merging.Alternatively, the second sub-step of the merging includes identifyingdata object level index node 592 for merging with the selected dataobject level index node to produce the two data object level index nodewhen leaf node 594 is the node selected for merging. In a third sub-stepof the merging, the two data object level index nodes are merged into atemporarily merged data object level index node 600 where thetemporarily merged data object level index node 600 includes a siblingentry (e.g., sibling dispersed storage network (DSN) address and siblingminimum index key) that is identical to the sibling entry of data objectlevel node 594 and includes data object entries of the two data objectlevel index nodes. In a fourth sub-step of the merging, the temporarilymerged data object level index node 600 is stored in the DSN.

FIG. 44J is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44Dthrough 44J. FIGS. 44H-J illustrate another example of the merging whenleaf node 592 or 594 is identified as the one data object level indexnode of the two data object index level nodes. In a fifth sub-step ofthe merging, the common parent index node 586 is updated to includeoverwriting a dispersed storage network (DSN) address of a child entryassociated with data object level index node 592 with a DSN address ofthe temporarily merged data object level index node 600 and to remove achild entry associated with data object level index node 594. In a sixthsub-step of the merging, the two data object level index nodes 592-594are deleted. When a change is detected of either the common parent indexnode 586 or the two data object level index nodes 592-594 subsequent tothe first sub-step of the merging and prior to final confirmation ofdeletion of the two data object level index nodes 592-594, the mergingincludes facilitating rolling back the updating of the common parentindex node 586 and the deletion of the two data object level index nodes592-594. The facilitating may also include deletion of the temporarilymerged data object level index node 600 from the DSN.

FIG. 44K is a schematic block diagram of another embodiment of adispersed storage system that includes a computing device 602 and adispersed storage network (DSN) 604. The DSN 604 may be implementedutilizing one or more of multiple computers, multiple computing devices,a DSN memory, a distributed storage and task network (DSTN), a DSTNmodule, and a plurality of storage nodes. The DSN 604 includes aplurality of DSN storage nodes 606. The plurality of DSN storage nodes606 includes at least one set of DSN storage nodes 606. Each DSN storagenode 606 of the plurality of DSN storage nodes 606 may be implementedutilizing at least one of a storage server, a storage unit, a dispersedstorage (DS) unit, a storage module, a memory device, a memory, adistributed storage and task (DST) execution unit, a user device, a DSTprocessing unit, and a DST processing module. The computing device 602may be implemented utilizing at least one of a server, a storage unit, aDSN storage node 606, a DS unit, a storage server, a storage module, aDS processing unit, a DS unit, a DST execution unit, a user device, aDST processing unit, and a DST processing module. For example, the DSN604 includes the computing device 602 when the computing device 602 isimplemented utilizing a DSN storage node 606. The computing device 602includes a dispersed storage (DS) module 608. The DS module 608 includesa determine module 610 and a merge module 612.

The system functions to determine to merge two data object level indexnodes and to merge the two data object level index nodes where ahierarchical ordered index structure includes the two data object levelindex nodes with regards to storage of a plurality of data objects inthe DSN 604. A plurality of data object index entries (e.g., each entryincluding a DSN address and a data object index key associated with adata object) is associated with the plurality of data objects, where theplurality of data object index entries is organized into thehierarchical ordered index structure in accordance with an ordering ofattributes of an attribute category.

With regards to merging the two data object level index nodes, thedetermine module 610 determines to merge the two data object level indexnodes. The determine module 610 determines to merge two data objectlevel index nodes by at least one of a variety of determine approaches.A first determine approach includes the determine module 610 determiningthat at least one of the two data object level index nodes includes toofew data object index entries. The determining includes the determinemodule 610 issuing slice requests 614 to the DSN 604, receiving sliceresponses 616 that includes slices of the two data object level indexnodes, and decoding slices to reproduce the two data object level indexnodes. The determine module 610 may further identify another of the twodata object level index nodes by identifying another data object levelindex when a sibling node (i.e. to the right of the one data objectlevel index node) exists, extract a DSN address of the sibling node.Alternatively, when a sibling node does not exist, the determine module610 identifies an adjacent data object level index node to the left byaccessing a parent node (e.g., a next level node) and identifying a DSNaddress of the adjacent data object level index node from the parentnode.

A second determine approach of the variety of determine approachesincludes the determine module 610 determining that at least one of thetwo data object level index nodes includes too few data object indexentry accesses in a given time frame. A third determine approachincludes the determine module 610 receiving a request 618 (e.g., afterdeleting several data objects associated with the data object levelindex node). A fourth determine approach includes the determine module610 determining that the hierarchical ordered index structure includestoo many data object level index nodes. A fifth determine approachincludes the determine module 610 detecting that an access performancelevel of the hierarchical ordered index structure compares unfavorablyto a minimum access performance level threshold (e.g., unfavorable whenaccess performance level is less than the minimum access performancelevel threshold). The detecting the access performance level includes atleast one of initiating a query, performing a test, accessing ahistorical performance record, and a lookup. The determining to mergethe two data object level index nodes may also include the determinemodule 610 outputting node information 620, where the node informationincludes identifiers for the two data object level index nodes, DSNaddresses of each of the two data object level index nodes, and the twodata object level index nodes (e.g., reconstructed from slices of sliceresponses 616).

With regards to merging the two data object level index nodes, the mergemodule 612, when the two data object level index nodes are to be merged,enters a loop that includes a series of loop steps. In a first loopstep, the merge module 612 merges the two data object level index nodesinto a temporarily merged data object level index node. The merge module612 merges the two data object level index nodes by a series of mergingsteps. A first merging step includes the merge module 612 aggregatingfirst data object index entries of a first one of the two data objectlevel index nodes with second data object index entries of a second oneof the two data object level index nodes to produce unsorted merged dataobject index entries. For example, the merge module 612 issues slicerequests 622 with regards to slices of the two data object level indexnodes, receives slice responses 624 that includes index slices of thetwo data object level index nodes, and decodes the index slices toreconstruct the two data object level index nodes. A second merging stepincludes the merge module 612 sorting the unsorted merged data objectindex entries in accordance with the ordering of attributes of theattribute category to produce sorted merged data object index entries. Athird merging step includes the merge module 612 generating an adjacentdata object level index node reference (e.g., a sibling index nodeentry) based on one or more adjacent data object level index nodes ofthe two data object level index nodes. For example, the merge module 612generates the reference to include a DSN address and a minimum dataobject index key for a sibling to the right of the two data object levelindex nodes when the sibling node exists, if not, the merge module 612generates a null sibling entry as the reference (e.g., that includes anull DSN address and null key).

In a second loop step, the merge module 612 performs a series ofsub-loop steps to initiate updating of the hierarchical ordered indexstructure. In a first sub-loop step, the merge module 612 identifies aDSN address for storing the temporarily merged data object level indexnode (e.g., generate a new DSN address). In a second sub-loop step, themerge module 612 sets up deletion of the two data object level indexnodes. The merge module 612 sets up deletion of the two data objectlevel index nodes by a series of deletion steps. A first deletion stepincludes the merge module 612 identifying a first set of DSN storagenodes 606 storing a first set of encoded index slices for a first one ofthe two data object level index nodes (e.g., based on a lookup). Asecond deletion step includes the merge module 612 identifying a secondset of DSN storage nodes storing a second set of encoded index slicesfor a second one of the two data object level index nodes (e.g., basedon a lookup, may be same or different as first set of DSN nodes). Athird deletion step includes the merge module 612 sending a first set ofdeletion commands to the first set of DSN storage nodes 606 (e.g., slicerequests 622 that includes checked writes for deleting). A fourthdeletion step includes the merge module 612 sending a second set ofdeletion commands to the second set of DSN storage nodes.

In a third sub-loop step, the merge module 612 sets up linking thetemporarily merged data object level index node to a next level node(e.g., a parent node) of the hierarchical ordered index structure. Themerge module 612 sets up linking the temporarily merged data objectlevel index node to the next level node by a series of linking steps. Afirst linking step includes the merge module 612 obtaining the DSNaddress for storing the temporarily merged data object level index node.A second linking step includes the merge module 612 identifying a firstentry of the next level node corresponding to a first one of the twodata object level index nodes (e.g., data object entry with a minimumindex key that sorts ahead of a second entry). A third linking stepincludes the merge module 612 identifying a second entry of the nextlevel node corresponding to a second one of the two data object levelindex nodes (e.g., data object entry with a minimum index key that sortsafter that of the first entry). A fourth linking step includes the mergemodule 612 requesting updating the first entry of the next level node byoverwriting a DSN address of the first one of the two data object levelindex nodes with the DSN address for storing the temporarily merged dataobject level index node. A fifth linking step includes the merge module612 requesting deleting of the second entry (e.g., not requiredanymore).

Alternatively, or in addition to, the merge module 612 may initiateupdating of the hierarchical ordered index structure to include a seriesof additional updating steps. In a first additional updating step, themerge module 612 sets up linking the temporarily merged data objectlevel index node to one or more adjacent data object level nodes of thehierarchical ordered index structure. For example, when the adjacentdata object level is in the structure to the left, the merge module 612overwrites a sibling entry within the adjacent data object level node tothe left with information of the temporarily merged data object levelindex node. In particular, the merge module 612 may set up linking thetemporarily merged data object level index node to one or more adjacentdata object level nodes by obtaining the DSN address for storing thetemporarily merged data object level index node and requesting updatingan entry of each of the one or more adjacent data object level nodes byoverwriting a DSN address for one of the two data object level indexnodes with the DSN address for storing the temporarily merged dataobject level index node. For example, the merge module 612 overwrites aprevious sibling DSN address with the DSN address for storing thetemporarily merged data object level index node facilitates storing ofthe adjacent data object level index node in the DSN based on a previousrevision number of the adjacent data object level index node. In asecond additional updating step of the series of additional updatingsteps, the merge module 612 determines, subsequent to merging the twodata object level index nodes, whether a change has occurred to the oneor more adjacent data object level nodes (e.g., any entry changed,added, or deleted). When a change has occurred, the merge module 612issues rollback transaction requests to the DSN with regards to previouswrite or delete transactions of the updating of the index structure.

In a fourth sub-loop step, the merge module 612 determines, subsequentto merging the two data object level index nodes, whether a change hasoccurred to at least one of one or more of the two data object levelindex nodes and the next level node (e.g., any entry changed, added, ordeleted). The merge module 612 determines whether the change hasoccurred to the one or more of the two data object level index nodes byat least one of a plurality of approaches. A first approach includes themerge module 612 receiving slice responses 624 that includes a firstrevision level discrepancy response from one or more of the first set ofDSN storage nodes 606 to indicate that the first one of the one or moreof the two data object level index nodes has changed. For example, themerge module 612 receives status codes from the first set of DSN storagenodes 606 indicating whether there is a checked write error due to arevision mismatch and indicating a change has not occurred when at leasta write threshold number of status codes indicate that there is nomismatch. A second approach includes the merge module 612 receiving asecond revision level discrepancy response from one or more of thesecond set of DSN storage nodes 606 to indicate that the second one ofthe one or more of the two data object level index nodes has changed.

In a third loop step of merging the two data object level index nodes,the merge module 612, when the change has occurred, repeats the loop.The merge module 612 repeats the loop by undoing the merging of thetemporarily merged data object level index node and undoing theinitiating of the updating of the hierarchical ordered index structureusing a checked write DSN process. The undoing includes issuing rollbacktransaction requests as slice requests 622 to the DSN 604 for eachpreviously issued write commands and/or delete commands included inslice requests 622.

Alternatively, in the third loop step, the merge module 612, when thechange has not occurred, commences the updating of the hierarchicalordered index structure. The merge module 612 commences the updating ofthe hierarchical ordered index structure by a series of commencingsteps. A first commencing step includes the merge module 612 dispersedstorage error encoding the temporarily merged data object level indexnode to produce a set of encoded merged index slices. A secondcommencing step includes the merge module 612 issuing a set of slicerequests 622 that includes a set of write commands to store the set ofencoded merged index slices at the DSN address for storing thetemporarily merged data object level index node. A third commencing stepincludes the merge module 612 issuing a first set of delete commands asslice requests 622 (e.g., checked write requests for deletion) to thefirst set of DSN storage nodes 606 that is storing a first set ofencoded index slices of a first one of the two data object level indexnodes. A fourth commencing step includes the merge module 612 issuing asecond set of delete commands to the second set of DSN storage nodes 606that is storing a second set of encoded index slices of a second one ofthe two data object level index nodes. A fifth commencing step includesthe merge module 612 reconstructing the next level node from a set ofnext level index slices (e.g., decode using dispersed storage errorcoding function). A sixth commencing step includes the merge module 612updating the reconstructed next level node to include the DSN address ofthe temporarily merged data object level index node to produce anupdated next level node. A seventh commencing step includes the mergemodule 612 dispersed storage error encoding the updated next level nodeto produce a set of updated next level index slices. The series ofcommencing steps may also include the merge module 612 facilitatingstorage of a write threshold number of the updated next level indexslices by issuing slice request 622 to the DSN 604 that includes theupdated next level index slices.

FIG. 44L is a flowchart illustrating an example of joining nodes of anindex. The method begins at step 630 where a processing module (e.g., ofa computer of a multiple computer dispersed storage network (DSN) thatstores a plurality of data objects) determines to merge two data objectlevel index nodes, where a plurality of data object index entries (e.g.,data object index key and DSN address of a corresponding data object) isassociated with the plurality of data objects. The plurality of dataobject index entries is organized into a hierarchical ordered indexstructure in accordance with an ordering of attributes of an attributecategory, where the hierarchical ordered index structure includes thetwo data object level index nodes.

The determining to merge two data object level index nodes includes atleast one of a variety of determination approaches. A firstdetermination approach includes determining that at least one of the twodata object level index nodes includes too few data object indexentries. For example, the processing module deletes one or more dataindex entries of the one of the two data object level index nodes,counts the number of data index entries, and compares the count to anentry threshold. For instance, the processing module determines that thenumber of data index entries in the one of the two data object levelindex nodes is less than the entry threshold when there are too few dataindex entries in the one of the two data object level index nodes.

The processing module may also identify another data object level indexnode of the two data object level index nodes based on identifying asibling index node or an adjacent data object level index node to theleft of the one data object level index node. A second determinationapproach includes determining that at least one of the two data objectlevel index nodes includes too few data object index entry accesses in agiven time frame. A third determination approach includes receiving arequest (e.g., after deleting several data objects associated with thedata object level index node). A fourth determination approach includesdetermining that the hierarchical ordered index structure includes toomany data object level index nodes. A fifth determination approachincludes detecting that an access performance level of the hierarchicalordered index structure compares unfavorably to a minimum accessperformance level threshold (e.g., unfavorable when access performancelevel is less than the minimum access performance level threshold).

When the two data object level index nodes are to be merged, the methodat step 632 enters a loop where the processing module merges the twodata object level index nodes into a temporarily merged data objectlevel index node. The merging the two data object level index nodesincludes a series of merging steps. A first merging step includesaggregating first data object index entries of a first one of the twodata object level index nodes with second data object index entries of asecond one of the two data object level index nodes to produce unsortedmerged data object index entries. A second merging step includes sortingthe unsorted merged data object index entries in accordance with theordering of attributes of the attribute category to produce sortedmerged data object index entries. A third merging step includesgenerating an adjacent data object level index node reference (e.g.,sibling entry) based on one or more adjacent data object level indexnodes of the two data object level index nodes. For example, theprocessing module generates a DSN address and minimum index key for asibling to the right of the two data object level index nodes when thesibling node exists, if not, the processing module generates a nullsibling entry as the reference (e.g., that includes a null DSN addressand null key).

The method continues at step 634 where the processing module initiatesupdating of the hierarchical ordered index structure where theprocessing module identifies a DSN address for storing the temporarilymerged data object level index node (e.g., generate a new DSN address).The method continues at step 636 where the processing module sets updeletion of the two data object level index nodes. The setting updeletion of the two data object level index nodes includes a series ofdeletion steps. A first deletion step includes identifying a first setof DSN storage nodes storing a first set of encoded index slices for afirst one of the two data object level index nodes (e.g., based on alookup). A second deletion step includes identifying a second set of DSNstorage nodes storing a second set of encoded index slices for a secondone of the two data object level index nodes (e.g., based on a lookup,may be same or different set of DSN nodes). A third deletion stepincludes sending a first set of deletion commands to the first set ofDSN storage nodes (e.g., checked write for deleting). A fourth deletionstep includes sending a second set of deletion commands to the secondset of DSN storage nodes.

The method continues at step 638 where the processing module sets uplinking the temporarily merged data object level index node to a nextlevel node (e.g., a parent node of the two data object level indexnodes) of the hierarchical ordered index structure. The setting uplinking the temporarily merged data object level index node to the nextlevel node includes a series of set up steps. A first set up stepincludes obtaining the DSN address for storing the temporarily mergeddata object level index node. A second set up step includes identifyinga first entry of the next level node corresponding to a first one of thetwo data object level index nodes (e.g., data object entry with aminimum index key that sorts ahead of a second entry). A third set upstep includes identifying a second entry of the next level nodecorresponding to a second one of the two data object level index nodes(e.g., data object entry with a minimum index key that sorts after thatof the first entry). A fourth set up step includes requesting updatingthe first entry of the next level node by overwriting a DSN address ofthe first one of the two data object level index nodes with the DSNaddress for storing the temporarily merged data object level index node.A fifth set up step includes requesting deleting of the second entry(e.g., not required anymore).

The method continues at step 640 where the processing module sets uplinking the temporarily merged data object level index node to one ormore adjacent data object level nodes of the hierarchical ordered indexstructure. The setting up linking the temporarily merged data objectlevel index node to one or more adjacent data object level nodesincludes a series of set up steps. A first set up step includesobtaining the DSN address for storing the temporarily merged data objectlevel index node. A second set up step includes requesting updating anentry of each of the one or more adjacent data object level nodes byoverwriting a DSN address for one of the two data object level indexnodes with the DSN address for storing the temporarily merged dataobject level index node. For example, the processing module overwrites aprevious sibling DSN address with the DSN address for storing thetemporarily merged data object level index node. The setting up furtherincludes storing the at least one of the one or more adjacent dataobject level nodes in the DSN based on a previous revision number of theother data object level index node (e.g., issuing checked write commandsto the DSN).

The method continues at step 642 where the processing module determines,subsequent to merging the two data object level index nodes, whether achange has occurred to the one or more adjacent data object level nodes(e.g., any entry changed, added, or deleted; as indicated by a revisionmismatch error message of a checked write responses). The methodcontinues at step 644 where the processing module determines, subsequentto merging the two data object level index nodes, whether a change hasoccurred to at least one of one or more of the two data object levelindex nodes and the next level node (e.g., any entry changed, added, ordeleted). The determining whether the change has occurred to the one ormore of the two data object level index nodes includes at least one of avariety of approaches. A first approach includes receiving a firstrevision level discrepancy response from one or more of the first set ofDSN storage nodes to indicate that the first one of the one or more ofthe two data object level index nodes has changed (e.g., a revisionmismatch indicator in a checked write response). A second approachincludes receiving a second revision level discrepancy response from oneor more of the second set of DSN storage nodes to indicate that thesecond one of the one or more of the two data object level index nodeshas changed. When the change has occurred (e.g., to one or more of theone or more adjacent data object level nodes, one or more of the twodata object level index nodes, and the next level node), the methodbranches to step 646.

When the change has not occurred the method continues to step 650. Whenthe change has not occurred, the method continues at step 650 where theprocessing module commences the updating of the hierarchical orderedindex structure. The commencing the updating of the hierarchical orderedindex structure includes a series of commencing steps. A firstcommencing step includes dispersed storage error encoding thetemporarily merged data object level index node to produce a set ofencoded merged index slices. A second commencing step includes issuing aset of write commands to store the set of encoded merged index slices atthe DSN address for storing the temporarily merged data object levelindex node. A third commencing step includes issuing a first set ofdelete commands to a first set of DSN storage nodes that is storing afirst set of encoded index slices of a first one of the two data objectlevel index nodes. A fourth commencing step includes issuing a secondset of delete commands to a second set of DSN storage nodes that isstoring a second set of encoded index slices of a second one of the twodata object level index nodes. A fifth commencing step includesreconstructing the next level node from a set of next level index slices(e.g., decode using dispersed storage error coding function). A sixthcommencing step includes updating the reconstructed next level node toinclude the DSN address of the temporarily merged data object levelindex node to produce an updated next level node. A seventh commencingstep includes dispersed storage error encoding the updated next levelnode to produce a set of updated next level index slices (e.g., may alsoinclude facilitating storage of a write threshold number of the updatednext level index slices).

When the change has occurred, the method continues at step 646, wherethe step includes the processing module undoing the merging of thetemporarily merged data object level index node. For example, theprocessing module facilitates deletion of the temporarily merged dataobject level index node. The method continues at step 648, where thestep includes the processing module undoing the initiating of theupdating of the hierarchical ordered index structure using a checkedwrite DSN process. For example, the processing module facilitatesissuing rollback transaction requests to the DSN for each previouslyissued write or delete command associated with the one or more adjacentdata object level nodes, the two data object level index nodes, and thenext level node. The method loops back to step 632 to start over.

FIG. 45A is a diagram illustrating another example of an index structureprior to splitting a node. An index structure diagram representing theindex structure after the splitting of the node is represented in FIG.45B. The index structure includes three nodes of an index. The threenodes includes an index node stored in a distributed storage and tasknetwork (DSTN) at a source name address of 4F7 and two leaf nodes storedat source name addresses 5AB and 52D, wherein the leaf node stored atsource name address 5AB is split in the example as illustrated in FIG.45B.

The index node includes a node type indicating not a leaf node (e.g.,index node), a sibling node source name pointing to an index node storedat source name 42C, a sibling minimum index key of “j”, a child 1 sourcename of 5AB, a child 1 minimum index key of “a”, a child 2 source nameof 52D, and a child 2 minimum index key of “d”. The leaf node stored atsource name 5AB includes a node type indicating a leaf node, a siblingnode source name pointing to the leaf node stored at source name 52D, asibling minimum index key of “d”, a data 1 source name of 76B, a data 1index key of “a”, a data 2 direct data entry (e.g., b39d5ac9), and adata 2 index key of “b”. The leaf node stored at source name 52Dincludes a node type indicating a leaf node, a sibling node source namepointing to a leaf node stored at source name 539, a sibling minimumindex key of “j”, a data 1 source name of 8F6, and a data 1 index key of“d”.

FIG. 45B is a diagram illustrating another example of an index structureafter splitting a leaf node illustrated in FIG. 45A. An index structurediagram representing the index structure prior to splitting the node isrepresented in FIG. 45A. The index structure after the splittingincludes four nodes of an index. The four nodes includes an index nodestored in a distributed storage and task network (DSTN) at a source nameaddress of 4F7, a leaf node stored at source name address 5AB that wassplit, a new leaf node stored at source name 9D4C that resulted from thesplit, and a leaf node stored at source name address 52D.

The new leaf node includes one or more data entries of the leaf nodestored at source name address 5AB. For example, half of the data entriesof the leaf node stored at source name address 5AB are included in thenew leaf node. The index node includes a node type indicating not a leafnode (e.g., index node), a sibling node source name pointing to an indexnode stored at source name 42C, a sibling minimum index key of “j”, achild 1 source name of 5AB and a child 1 minimum index key of “a”, achild 2 source name of 9D4C and a child 2 minimum index key of “b”, anda child 3 source name of 52D and a child 3 minimum index key of “d”. Theleaf node stored at source name 5AB includes a node type indicating aleaf node, a sibling node source name pointing to the new leaf nodestored at source name 9D4C, a sibling minimum index key of “b”, and adata 1 source name of 76B and a data 1 index key of “a”. The leaf nodestored at source name 9D4C includes a node type indicating a leaf node,a sibling node source name pointing to the leaf node stored at sourcename 52D, a sibling minimum index key of “d”, and a data 2 direct dataentry (e.g., b39d5ac9) and a data 2 index key of “b”. The leaf nodestored at source name 52D includes a node type indicating a leaf node, asibling node source name pointing to a leaf node stored at source name539, a sibling minimum index key of “j”, and a data 1 source name of 8F6and a data 1 index key of “d”.

FIG. 45C is a diagram illustrating an example of an index structure of astarting step of a series of example steps depicted in FIGS. 44C and44D. The index structure includes common parent index node 586, leafnode 588, leaf node 592, and leaf node 594, as discussed with referenceto FIG. 44C. The index structure also includes leaf node 650 whichincludes the contents of leaf node 590 and the data object entriescorresponding to an index keys of H. Smith and K. Smith. FIGS. 45C-Dillustrate an example of splitting leaf node 650. In a first sub-step ofthe splitting, data object level index node (e.g., leaf node) 650 isselected for splitting (e.g., since node 650 includes too many dataobject entries). In a second sub-step of the splitting, one or more dataobject entries of the selected data object level index node 650 areidentified for splitting out. In a third sub-step of the splitting, anew sibling node 652, with respect to the data object level index node650, is generated to include the one or more identified data objectentries and a sibling entry of data object level index node 650 (e.g.,pointing to a leaf node 592 to include a DSN address of leaf node 592and a minimum index key of L. Smith with regards to leaf node 592). In afourth sub-step of the splitting, the new sibling node 652 is stored ina dispersed storage network as a set of sibling index node slices.

FIG. 45D is a diagram illustrating an example of the index structure ofanother step of the series of example steps depicted in FIGS. 44C and44D. The index structure includes common parent index node 586 and fiveleaf nodes subsequent to inserting of another leaf node. The five leafnodes includes leaf node 588, selected for splitting data object levelindex node 650, new sibling data index node 652, leaf node 592, and leafnode 594. In a fifth sub-step of the splitting, a sibling entry of theselected data object level index node 650 is updated to include asibling DSN address of new sibling data object index node 652 and aminimum data object index key associated with the new sibling dataobject index key 652. In a sixth sub-step of the splitting, identifiedone or more data object entries of the selected data object level indexnode 650 are removed from the selected data object level index node 650.

In a seventh sub-step of the splitting, the selected data object levelindex node 650 is stored in a dispersed storage network (DSN). In aneighth sub-step of the splitting, the common parent index node 586 isupdated to include a new child entry corresponding to the new siblingdata object index node 652 (e.g., to include the sibling DSN address ofnew sibling data object index node 652 and the minimum data object indexkey associated with the new sibling data object index key 652. In aninth sub-step of the splitting, the common parent data object indexnode 652 is stored in the DSN.

In a tenth sub-step of the splitting, a determination is made todetermine whether any changes have occurred during the splitting processto at least one of the selected data object index node 650 and thecommon parent data object index node 586. When a change is detected,rollback transaction commands are sent to the DSN with regards tostorage of the selected data object index level node 650 and the commonparent data object index node 586. In addition, when change is detected,the new sibling data object index node 652 may be deleted. The method tosplit a selected data object level index node to remove one or more dataobject entries creating a new sibling data object index level node isdiscussed in greater detail with reference to FIGS. 45E and 45F.

FIG. 45E is a schematic block diagram of another embodiment of adispersed storage system that includes a computing device 654 and adispersed storage network (DSN) 656. The DSN 656 may be implementedutilizing one or more of multiple computers, multiple computing devices,a DSN memory, a distributed storage and task network (DSTN), a DSTNmodule, and a plurality of storage nodes. The DSN 656 includes aplurality of DSN storage nodes 658. The plurality of DSN storage nodes658 includes at least one set of DSN storage nodes 658. Each DSN storagenode 658 of the plurality of DSN storage nodes 658 may be implementedutilizing at least one of a storage server, a storage unit, a dispersedstorage (DS) unit, a storage module, a memory device, a memory, adistributed storage and task (DST) execution unit, a user device, a DSTprocessing unit, and a DST processing module. The computing device 654may be implemented utilizing at least one of a server, a storage unit, aDSN storage node 658, a DS unit, a storage server, a storage module, aDS processing unit, a DS unit, a DST execution unit, a user device, aDST processing unit, and a DST processing module. For example, the DSN656 includes the computing device 654 when the computing device 654 isimplemented utilizing a DSN storage node 658. The computing device 654includes a dispersed storage (DS) module 660. The DS module 660 includesa determine module 662 and a split module 664.

The system functions to determine to remove data object index entriesfrom a data object level index node and to establish a new data objectlevel index node that includes identified data object entries forremoval from the data object level index node, where a hierarchicalordered index structure includes the data object level index node withregards to storage of a plurality of data objects in the DSN 656. Aplurality of data object index entries (e.g., each entry including a DSNaddress and a data object index key associated with a data object) isassociated with the plurality of data objects, where the plurality ofdata object index entries is organized into the hierarchical orderedindex structure in accordance with an ordering of attributes of anattribute category.

With regards to the determining to remove the data object entries fromthe data object level index node, the determine module 662 determines toremove data object index entries from a data object level index node.The determine module 662 determines to remove data object index entriesfrom the data object level index node by at least one of a variety ofapproaches. A first approach includes the determine module 662determining that the data object level index node includes too many dataobject index entries (e.g., counting the number of data object entriesand comparing to a data object entry maximum threshold). The determiningincludes issuing slice requests 666 to the DSN 656 requesting encodedindex slices of the data object level index node, receiving sliceresponses 668 that includes at least a decode threshold number ofencoded index slices, decoding the at least the decode threshold numberof encoded index slices to reproduce the data object level index node,and counting the number of data object entries.

A second approach to determine to remove data object index entries fromthe data object level index node includes the determine module 662determining that the data object level index node includes too many dataobject index entry accesses in a given time frame. A third approachincludes the determine module 662 receiving a request 670 (e.g., afteradding several data object entries associated with several data objectscorresponding to the data object level index node). A fourth approachincludes the determine module 662 determining that the hierarchicalordered index structure includes too few data object level index nodes(e.g., identifying a number of spans of the data object level andcomparing the number of spans to a minimum span threshold). A fifthapproach includes the determine module 662 detecting that an accessperformance level of the hierarchical ordered index structure comparesunfavorably to a minimum access performance level threshold (e.g.,unfavorable when access performance level is less than the minimumaccess performance level threshold). The determining to remove the dataobject entries from the data object level index node may also includeissuing node information 672 to the split module 664. The nodeinformation 672 includes one or more of the data object index entries, aDSN address associated with the data object level index node, and thedata object level index node.

With regards to establishing the new data object level index node thatincludes identified data object entries for removal from the data objectlevel index node, when the data object index entries of the data objectlevel index node are to be removed, the split module 664 enters a loopthat causes the split module 664 to perform a series of loop steps. In afirst loop step, the split module 664 identifies data object indexentries of the data object level index node to extract. For example, thesplit module 664 issues slice requests 674 to the DSN 656 with regardsto retrieving encoded index slices, receives slice responses 676 thatincludes at least a decode threshold number of encoded index slices, anddecodes the at least the decode threshold number of encoded index slicesto reproduce the data object level index node, and identifies upperportion entries (e.g., half, a number to satisfy a reduction goal) of asorted list of data object index entries. In a second loop step, thesplit module 664 creates a temporary sibling data object level indexnode to include the identified extracted data object index entries. Thegenerating the temporary sibling data object level index node may alsoinclude generating a sibling entry of a sibling node to the temporarysibling data object level index node when the sibling node exists byreusing a sibling entry of the data object level index node.

In a third loop step, the split module 664 initiates updating of thehierarchical ordered index structure where the split module 664 performsa series of sub-loop steps. In a first sub-loop step, the split module664 identifies a DSN address for storing the temporary sibling dataobject level index node (e.g., generate a new DSN address). In a secondsub-loop step, the split module 664 sets up linking the temporarysibling data object level index node to a next level node (e.g., parentnode) of the hierarchical ordered index structure. The split module 664sets up linking the temporary sibling data object level index node tothe next level node by a series of setup steps. A first setup stepincludes the split module 664 obtaining the DSN address for storing thetemporary sibling data object level index node. A second setup stepincludes the split module 664 generating a new child node entrycorresponding to the temporary sibling data object level index node thatincludes the DSN address for storing the temporary sibling index node.The generating may further include including a minimum data object indexkey of the temporary sibling index node. A third setup step includes thesplit module 664 requesting updating the next level node to include thenew child node entry (e.g., generate slices, issue checked write slicerequests 674 that includes the slices).

In a third sub-loop step, the split module 664 sets up linking thetemporary sibling data object level index node to the data object levelindex node and to an adjacent data object level index node. For example,the setting up includes updating the temporary sibling data object levelindex node to include a DSN address of the data object level index nodeand a DSN address of another sibling data object level index node (e.g.,the adjacent data object level index node) to the right from thetemporary sibling data object level index node).

Prior to removing data object index entries from the data object levelindex node, in a fourth sub-loop step, the split module 664 determineswhether a change has occurred (e.g., any entry changed, added, ordeleted entry) to at least one of the data object level index node, theadjacent data object level index node, and the next level node. Thesplit module 664 determines whether the change has occurred to the dataobject level index node by receiving a revision level discrepancyresponse of slice responses 676 from at least one of a set of DSNstorage nodes to indicate that the data object level index node haschanged. The split module 664 receives a slice responses 676 thatincludes the revision level discrepancy response in response to issuingone or more slice request 674 with regards to writing any encoded indexslice to the DSN 656. The receiving includes receiving a status codeswithin slice responses 676 from the set of DSN storage nodes 658indicating whether there is a checked write error due to a revisionmismatch and indicating a change has not occurred when at least a writethreshold number of status codes indicate that there is no mismatch. Thereceiving may further include performing similar mismatch checks for thenext level node.

In a fourth loop step, when the change has occurred, the split module664 repeats the loop. The split module 664 repeats the loop by undoingthe initiating of the updating of the hierarchical ordered indexstructure using a checked write DSN process. The undoing furtherincludes issuing rollback transaction requests as slice requests 674 tothe DSN 656 for each issued write command with regards to storage of thedata object level index node, the next level node, and the temporarysibling data object level index node.

Alternatively, in the fourth loop step, when the change has notoccurred, the split module 664 removes the data object index entriesfrom the data object level index node and commences the updating of thehierarchical ordered index structure (e.g., finishing writing thetemporary sibling data object level index node to the DSN 656, writingthe updated data object level index node to the DSN, write an updatednext level node to the DSN). The split module 664 removes the dataobject index entries from the data object level index node by a seriesof removal steps. A first removal step includes the split module 664dispersed storage error decoding a set of encoded data object indexslices of slice responses 676 to recapture the data object level indexnode. For example, the split module 664 retrieves the set of encodeddata object index slices within slice responses 676 and decodes the setof encoded data object index slices using a dispersed storage errorcoding function to produce the data object level index node. A secondremoval step includes the split module 664 deleting the data objectindex entries from the recaptured data object level index node toproduce a reduced data object level index node.

A third removal step includes the split module 664 updating an adjacentdata object level index node identifier (e.g., a sibling entry thatincludes a DSN address and a minimum data object index key associatedwith the temporary sibling data object level index node) of the reduceddata object level index node to identify the temporary sibling dataobject level index node to produce an updated data object level indexnode. A fourth removal step includes the split module 664 dispersedstorage error encoding the updated data object level index node toproduce a set of encoded updated data object index slices. A fifthremoval step includes the split module 664 issuing a set ofchecked-write commands as slice requests 674 to store the set of encodedupdated data object index slices in the DSN 656. The issuing may includegenerating and outputting the set of checked-write commands as slicerequests 674 to include a revision number from corresponding read sliceresponses 676 of the dispersed storage error decoding step.Alternatively, the generating may include generating at least one of aset of version verification requests and a set of temporary writerequests.

The split module 664 commences the updating of the hierarchical orderedindex structure by a series of commencing steps. A first commencing stepincludes the split module 664 dispersed storage error encoding thetemporary sibling data object level index node to produce a set ofencoded sibling index slices. A second commencing step includes thesplit module 664 issuing a set of write commands as slice requests 674to store the set of encoded sibling index slices at the DSN address forstoring the temporary sibling index node. A third commencing stepincludes the split module 664 reconstructing the next level node from aset of next level index slices (e.g., retrieve slices and decode usingthe dispersed storage error coding function). A fourth commencing stepincludes the split module 664 updating the reconstructed next level nodeto include an identifier (e.g., a child node entry that includes the DSNaddress for storing the temporary sibling index node and the minimumdata object index key associated with the temporary sibling index node)to identify the temporary sibling data object level index node toproduce an updated next level node. A fifth commencing step includes thesplit module 664 dispersed storage error encoding the updated next levelnode to produce a set of updated next level index slices. The encodingmay further include facilitating storage of a write threshold number ofthe updated next level index slices.

FIG. 45F is a flowchart illustrating an example of splitting nodes of anindex. The method begins at step 680 where a processing module (e.g., ofa computer of a multiple computer dispersed storage network (DSN) thatstores a plurality of data objects) determines to remove data objectindex entries from a data object level index node, where a plurality ofdata object index entries (e.g., data object index key and DSN addressof a corresponding data object) is associated with the plurality of dataobjects. The plurality of data object index entries is organized into ahierarchical ordered index structure in accordance with an ordering ofattributes of an attribute category, where the hierarchical orderedindex structure includes the data object level index node.

The determining to remove data object index entries from the data objectlevel index node includes at least one of a variety of approaches. Afirst approach includes determining that the data object level indexnode includes too many data object index entries. The determiningincludes counting a number of data object entries and comparing thenumber to a data object entry maximum threshold. A second approachincludes determining that the data object level index node includes toomany data object index entry accesses in a given time frame. A thirdapproach includes receiving a request. For example, a request isreceived subsequent to the addition of several data object entriesassociated with several data objects corresponding to the data objectlevel index node. A fourth approach includes determining that thehierarchical ordered index structure includes too few data object levelindex nodes. The determining includes identifying a number of spans ofthe data object level and comparing the number of spans to a minimumspan threshold. A fifth approach includes detecting that an accessperformance level of the hierarchical ordered index structure comparesunfavorably to a minimum access performance level threshold. Forexample, the processing module detects that the access performance levelis below the minimum access performance threshold when an accessbottleneck has occurred associated with the data object level indexnode.

When the data object index entries of the data object level index nodeare to be removed, the method continues at step 682 where the processingmodule enters a loop that includes the processing module identifyingdata object index entries of the data object level index node toextract. For example, the processing module identifies data objectentries associated with an upper portion (e.g., half, a number tosatisfy a goal) of a sorted list of data object index entries of thedata object level index node. The method continues at step 684 where theprocessing module creates a temporary sibling data object level indexnode to include the identified extracted data object index entries. Forexample, the processing module generates the temporary sibling dataobject level index node to also include a sibling entry for a siblingnode to the temporary sibling data object level index node when thesibling node exists by reusing a sibling entry of the data object levelindex node.

The method continues at step 686 where the processing module initiatesupdating of the hierarchical ordered index structure which includes theprocessing module identifying a DSN address for storing the temporarysibling data object level index node (e.g., generate a new DSN address).The method continues at step 688 where the processing module sets uplinking the temporary sibling data object level index node to a nextlevel node (e.g., parent node) of the hierarchical ordered indexstructure. The setting up linking the temporary sibling data objectlevel index node to the next level node includes a series of setupsteps. A first setup step includes obtaining the DSN address for storingthe temporary sibling data object level index node. A second setup stepincludes generating a new child node entry corresponding to thetemporary sibling data object level index node that includes the DSNaddress for storing the temporary sibling index node. The generating mayfurther include including a minimum data object index key of thetemporary sibling index node. A third setup step includes requestingupdating the next level node to include the new child node entry. Forexample, the processing module generate slices and issues checked writeslice requests that includes the slices.

The method continues at step 690 where the processing module sets uplinking the temporary sibling data object level index node to the dataobject level index node and to an adjacent data object level index node.For example, the setting up includes updating the temporary sibling dataobject level index node to include a DSN address of the data objectlevel index node and a DSN address of another sibling data object levelindex node (e.g., the adjacent data object level index node) to theright from the temporary sibling data object level index node.

Prior to removing data object index entries from the data object levelindex node, the method continues at step 692 where the processing moduledetermines whether a change has occurred to at least one of the dataobject level index node, the adjacent data object level index node, andthe next level node (e.g., any entry changed, added, or deleted entry).The determining whether the change has occurred to the data object levelindex node includes receiving a revision level discrepancy response fromat least one of a set of DSN storage nodes to indicate that the dataobject level index node has changed. The receiving includes receiving astatus codes from the set of DSN storage nodes indicating whether thereis a checked write error due to a revision mismatch and indicating achange has not occurred when at least a write threshold number of statuscodes indicate that there is no mismatch. The receiving may also includeperforming similar mismatch checks for the next level node.

When the change has not occurred, the method branches to step 696. Whenthe change has occurred, the method continues to step 694. The methodcontinues at step 694 where the processing module repeats the loop whererepeating the loop includes undoing the initiating of the updating ofthe hierarchical ordered index structure using a checked write DSNprocess. The undoing includes issuing rollback transaction requests tothe DSN for each issued write command with regards to storage of thedata object level index node, the next level node, and the temporarysibling data object level index node. The method branches back to step682 to try again.

When the change has not occurred, the method continues at step 696 wherethe processing module removes the data object index entries from thedata object level index node. The removing the data object index entriesfrom the data object level index node includes a series of removingsteps. A first removing step includes dispersed storage error decoding aset of encoded data object index slices to recapture the data objectlevel index node. For example, the processing module retrieves the setof encoded data object index slices and decodes the set of encoded dataobject index slices using a dispersed storage error coding function toproduce the data object level index node. A second removing stepincludes deleting the data object index entries from the recaptured dataobject level index node to produce a reduced data object level indexnode.

A third removing step includes updating an adjacent data object levelindex node identifier (e.g., a sibling entry that includes a DSN addressand a minimum data object index key associated with the temporarysibling data object level index node) of the reduced data object levelindex node to identify the temporary sibling data object level indexnode to produce an updated data object level index node. A fourthremoving step includes dispersed storage error encoding the updated dataobject level index node to produce a set of encoded updated data objectindex slices. A fifth removing step includes issuing a set ofchecked-write commands to store the set of encoded updated data objectindex slices in the DSN. The issuing may include generating andoutputting the set of checked-write commands to include a revisionnumber from corresponding read slice responses of the dispersed storageerror decoding step. Alternatively, the generating may includegenerating at least one of a set of version verification requests and aset of temporary write requests.

The method continues at step 698 where the processing module commencesthe updating of the hierarchical ordered index structure. The commencingincludes finishing writing the temporary sibling data object level indexnode to the DSN, writing the updated data object level index node to theDSN, and writing an updated next level node to the DSN. The commencingthe updating of the hierarchical ordered index structure furtherincludes a series of updating steps. A first updating step includesdispersed storage error encoding the temporary sibling data object levelindex node to produce a set of encoded sibling index slices. A secondupdating step includes issuing a set of write commands to store the setof encoded sibling index slices at the DSN address for storing thetemporary sibling index node. A third updating step includesreconstructing the next level node from a set of next level indexslices. For example, the processing module retrieves slices and decodesthe slices using the dispersed storage error coding function toreproduce the next level node. A fourth updating step includes updatingthe reconstructed next level node to include an identifier; (e.g., achild node entry that includes the DSN address for storing the temporarysibling index node and the minimum data object index key associated withthe temporary sibling index node) to identify the temporary sibling dataobject level index node to produce an updated next level node. A fifthupdating step includes dispersed storage error encoding the updated nextlevel node to produce a set of updated next level index slices. Theencoding may also include facilitating storage of a write thresholdnumber of the updated next level index slices.

FIG. 46A is a diagram illustrating another example of an index structureprior to expansion (e.g., increasing depth) of an associated index. Anindex structure diagram representing the index structure after theincreasing of the depth is represented in FIG. 46B. The index structureincludes four nodes of an index. The four nodes includes an index node(e.g., a root node) stored in a distributed storage and task network(DSTN) at a source name address of 4F7, a first leaf node stored atsource name address 5AB, a second leaf node stored at source name 9D4C,and a third leaf node stored at source name address 52D.

The index node includes a node type indicating not a leaf node (e.g.,index node), a sibling node source name pointing of null (e.g., nosibling since a root node), a sibling minimum index key of null, a child1 source name of 5AB and a child 1 minimum index key of “a”, a child 2source name of 9D4C and a child 2 minimum index key of “b”, and a child3 source name of 52D and a child 3 minimum index key of “d”. The firstleaf node stored at source name 5AB includes a node type indicating aleaf node and a sibling node source name pointing to the second leafnode stored at source name 9D4C etc. The second leaf node stored atsource name 9D4C includes a node type indicating a leaf node and asibling node source name pointing to the third leaf node stored atsource name 52D etc. The third leaf node stored at source name 52Dincludes a node type indicating a leaf node and a sibling node sourcename pointing to a leaf node stored at source name 539 etc. The indexnode stored at source name 4F7 may include too many entries requiringdepth of the index to be increased by splitting the index node into twonew index nodes and generating a new root node above the two new indexnodes as illustrated in FIG. 46B.

FIG. 46B is a diagram illustrating another example of an index structureafter increasing depth of an associated index. The index structure ofthe index prior to increasing the depth as illustrated in FIG. 46A. Theindex structure includes six nodes of the index. The six nodes includesa new root node (e.g., an index node type) stored in a distributedstorage and task network (DSTN) at a source name address of 6F5, a firstnew index node stored at a source name address of 6D2, a second newindex node stored at a source name address of 6E9, a first leaf nodestored at source name address 5AB, a second leaf node stored at sourcename 9D4C, and a third leaf node stored at source name address 52D.

The index node FIG. 46A is split into the two new index nodes. The firstnew index node stored at the source name address of 6D2 includes a nodetype indicating not a leaf node (e.g., index node), a sibling nodesource name pointing to the second new index node stored at source nameaddress 6E9, a sibling minimum index key of “d”, a child 1 source nameof 5AB and a child 1 minimum index key of “a”, and a child 2 source nameof 9D4C and a child 2 minimum index key of “b”. The second new indexnode stored at the source name address of 6E9 includes a node typeindicating not a leaf node (e.g., index node), a sibling node sourcename of null, a sibling minimum index key of null and a child 1 sourcename of 52D and a child 1 minimum index key of “d”. The root nodeincludes a node type indicating not a leaf node (e.g., index node), asibling node source name pointing of null (e.g., no sibling since a rootnode), a sibling minimum index key of null, a child 1 source name of 6D2and a child 1 minimum index key of “a” and a child 2 source name of 6E9and a child 2 minimum index key of “d”.

The first second and third leaf nodes are as in FIG. 46A prior to theincreasing of the depth of the index, wherein the first leaf node storedat source name 5AB includes a node type indicating a leaf node and asibling node source name pointing to the second leaf node stored atsource name 9D4C etc. The second leaf node stored at source name 9D4Cincludes a node type indicating a leaf node and a sibling node sourcename pointing to the third leaf node stored at source name 52D etc. Thethird leaf node stored at source name 52D includes a node typeindicating a leaf node and a sibling node source name pointing to a leafnode stored at source name 539 etc. The method to increase the depth ofthe index is discussed in greater detail with reference to FIG. 46C.

FIG. 46C is a diagram illustrating an example of an index structure ofan example of expanding the index structure in example steps depicted inFIGS. 46D and 46E. The index structure includes a root index node 700and immediate child index nodes 701, 702, 703. Immediate child indexnode 703 includes a null sibling entry and child entries associated witha minimum index key of L. Smith and another minimum index key of N.Smith. Immediate child index node 702 includes a sibling entryassociated with immediate child index node 703 (e.g., a minimum indexkey of L. Smith) and child entries associated with a minimum index keyof F. Smith and another minimum index key of J. Smith. Immediate childindex node 701 includes a sibling entry associated with immediate childindex node 702 (e.g., a minimum index key of F. Smith) and child entriesassociated with a minimum index key of A. Smith and another minimumindex key of E. Smith. The root index node 700 includes a null siblingentry, an immediate child entry for immediate child index node 701 thatincludes a minimum index key of A. Smith, another immediate child entryfor immediate child index node 702 that includes a minimum index key ofF. Smith, and yet another immediate child entry for immediate childindex node 703 that includes a minimum index key of L. Smith.

When determining to expand the index structure (e.g., the root indexnode has too many entries), the index structure is expanded from the topwhere the expanding includes replacing the root index node with a newroot index node and two or more sub-root index nodes. A series of stepsto depict the expanding are discussed in greater detail with referenceto FIGS. 46D-E.

FIG. 46D is a diagram illustrating an example of an index structure of astarting step of a series of example steps of expanding the indexstructure depicted in FIGS. 46D and 46E. The example includes indexstructure of FIG. 46C (e.g., including root index node 700 and immediatechild nodes 701-703) a new sibling node 704, a new index node 705, and anew root index node 706. In a first step of the expanding, adetermination is made to add a level to the index structure. In a secondstep of the expanding, a plurality of child entries is obtained from theroot index node 700. The obtaining may further include obtaining arevision number associated with root index node slices stored in adispersed storage network associated with the root index node 700. In athird step of the expanding, child node entries of the plurality ofchild node entries are selected to be associated with the new index node705 (e.g., dividing to achieve a structure goal). In a fourth step ofthe expanding, the new sibling node 704 is created to include a nullsibling entry and remaining child node entries of the plurality of childnode entries (e.g., entry associated with minimum index key L Smith forimmediate child index node 703). In a fifth step of the expanding, adispersed storage network (DSN) address of the new sibling node 704 isobtained (e.g., generated).

In a sixth step of the expanding, the new index node 705 is created toinclude a sibling entry corresponding to the new sibling node 704 (e.g.,to include the DSN address of the new sibling node 704 and the minimumindex key of L. Smith) and the selected child node entries (e.g., anentries associated with minimum index key A. Smith for immediate childindex node 701 and an entry associated with minimum index key F. Smithfor immediate child index node 702). In a seventh step of the expanding,a DSN address of the new index node 705 is obtained.

In an eighth step of the expanding, the new sibling node 704 is storedin the DSN using the DSN address of the new sibling node 704 and the newindex node 705 is stored in the DSN using the DSN address of the newindex node 705. In a ninth step of the expanding, the new root indexnode 706 is created to include a null sibling entry, a sub-root entryfor the new sibling node 704 (e.g., to include the minimum index key ofL. Smith and the DSN address of the new sibling node 704), and asub-root entry for the new index node 705 (e.g., to include the minimumindex key of A. Smith and the DSN address of the new index node 705). Adiscussion of the expanding example is continued with regards to FIG.46E.

FIG. 46E is a diagram illustrating an example of the index structure ofanother step of the series of example steps of expanding the indexstructure depicted in FIGS. 46D and 46E. The example includes continuingthe expanding process example of FIG. 46D and includes immediate childnodes 701-703) the new sibling node 704, the new index node 705, and thenew root index node 706. In a tenth step of the expanding, adetermination is made as to whether any changes have occurred to theroot index node 700 of FIG. 46D since the expanding process wasinitiated (e.g., compare a previously retrieved copy of root index node700 to a current retrieved copy of the root index node 700, utilize acheck-write process). When changes have occurred, undoing (e.g., issuingrollback commands corresponding to previous write slice requests to adispersed storage network) is performed to undo the storing of the newroot index node 706, the new index node 705, and the new sibling node704 and the expanding process is restarted. When changes have notoccurred, the index structure is updated to include finalizing writingof the new root index node 706 to the DSN and activating the new rootindex node (e.g., updating metadata of the index structure to includethe DSN address of the root index node 706 and to exclude the DSNaddress of the root index node 700 when different). In addition, theroot index node 700 may be deleted from the DSN.

FIG. 46F is a diagram illustrating an example of expanding an index andincludes index structures 708, 710, and 712. The expanding examplestarts with the index structure 708, transitions through the indexstructure 710, and concludes with index structure 712. Index structure708 includes a root index node 715 and a plurality of leaf nodes 714.Index structure 710 includes a new root index node 717, at least twosub-root index node 716, and another plurality of leaf nodes 714. Indexstructure 712 includes another new root index node 719, another at leasttwo sub-root index node 718, a plurality of sub-root index nodes 716that includes the at least two sub-root index node 716 of indexstructure 710, and yet another plurality of leaf nodes 714.

From time to time more leaf nodes 714 may be added to the plurality ofleaf nodes 714 of index structure 708. For example, more data objectsare added to a dispersed storage network and more corresponding dataobject entries are added to leaf nodes 714 of the plurality of leaf node714. A determination is made whether to expand the index structure 708as the index structure 708 grows (e.g., horizontally). For example, thedetermination is made to expand the index structure 708 when theplurality of leaf nodes 714 includes too many leaf nodes 714. When thedetermination is made to expand the index structure 708, the two or moresub-root index nodes 716 are created to include entries associated withthe new plurality of leaf nodes 714. The new root index node 716 iscreated to include sub-root entries corresponding to the two or moresub-root index nodes 716.

From time to time more leaf nodes 714 may be added to the plurality ofleaf nodes 714 of index structure 710. For example, more data objectsare added to the dispersed storage network, more corresponding dataobject entries are added to leaf nodes 714 of the plurality of leaf node714, and sub-root index node 716 are split to produce more than the atleast two sub-root index node 716. A determination is made whether toexpand the index structure 710 as the index structure 710 grows (e.g.,horizontally). For example, the determination is made to expand theindex structure 710 when the two or more sub-root index node 716includes too many sub-root index node 716 (e.g., new root index node 717includes too many entries). When the determination is made to expand theindex structure 710, the two or more sub-root index nodes 718 arecreated to include entries associated with the sub-root index nodes 716.The new root index node 719 is created to include sub-root entriescorresponding to the two or more sub-root index nodes 718. Such anexpansion process may continue indefinitely.

FIG. 46G is a schematic block diagram of another embodiment of adispersed storage system that includes a computing device 720 and adispersed storage network (DSN) 722. The DSN 722 may be implementedutilizing one or more of multiple computers, multiple computing devices,a DSN memory, a distributed storage and task network (DSTN), a DSTNmodule, and a plurality of storage nodes. The DSN 722 includes aplurality of DSN storage nodes 724. The plurality of DSN storage nodes724 includes at least one set of DSN storage nodes 724. Each DSN storagenode 724 of the plurality of DSN storage nodes 724 may be implementedutilizing at least one of a storage server, a storage unit, a dispersedstorage (DS) unit, a storage module, a memory device, a memory, adistributed storage and task (DST) execution unit, a user device, a DSTprocessing unit, and a DST processing module. The computing device 720may be implemented utilizing at least one of a server, a storage unit, aDSN storage node 724, a DS unit, a storage server, a storage module, aDS processing unit, a DS unit, a DST execution unit, a user device, aDST processing unit, and a DST processing module. For example, the DSN722 includes the computing device 720 when the computing device 720 isimplemented utilizing a DSN storage node 724. The computing device 720includes a dispersed storage (DS) module 726. The DS module 726 includesa determine module 728 and an expand module 730.

The system functions to determine to expand a hierarchical ordered indexstructure of a plurality of data object index entries and to expand thehierarchical ordered index structure. The hierarchical ordered indexstructure is in accordance with an ordering of attributes of anattribute category and the plurality of data object index entries isstored in a multitude of data object level index nodes and is associatedwith the plurality of data objects.

With regards to the determining to expand the hierarchical ordered indexstructure, the determine module 728 determines to expand thehierarchical ordered index structure of the plurality of data objectindex entries (e.g., each entry includes a data object index key and DSNaddress of a corresponding data object). The determine module 728determines to expand the hierarchical ordered index structure by atleast one of a variety of determining approaches. A first determiningapproach includes the determine module 728 determining that the rootindex node includes too many entries of the immediate children indexnodes. The immediate children index nodes includes intermediate indexnodes of the hierarchical ordered index structure, where theintermediate index nodes hierarchically lie between the root index nodeand the data object level index nodes. The immediate children indexnodes further includes at least some of the multitude of data objectlevel index nodes. The determining includes generating slice requests732 based on a DSN address of the root index node, outputting the slicerequests to the set of DSN storage nodes 724, receiving slice responses734 that includes encoded root index node slices and a revision numberindicator associated with the slices, and decoding the encoded rootindex node slices to reproduce the root index node. In an example ofdetermining that the root index node includes too many entries, thedetermining includes counting a number of immediate child node entriesand comparing to a child node entry maximum threshold. A seconddetermining approach includes the determine module 728 determining thatthe root index node is being accessed too frequently in a given timeframe.

A third determining approach, to determine to expand the hierarchicalordered index structure, includes the determine module 728 receiving arequest 736 (e.g., after adding several data object entries associatedwith several data objects corresponding to the hierarchical orderedindex structure). A fourth determining approach includes the determinemodule 728 detecting that an access performance level of thehierarchical ordered index structure compares unfavorably to a desiredaccess performance level threshold (e.g., unfavorable when accessperformance level is less than the minimum access performance levelthreshold). A fifth determining approach includes the determine module728 determining an estimated growth rate of the hierarchical orderedindex structure and expanding in accordance with the estimated growthrate. For example, the determine module 728 determines that at least oneindex node of at least one level of the index structure includes toomany child node entries. As another example, the determine module 728determines that the at least one level of the index structure includestoo many index nodes (e.g., span is too wide). For instance, thedetermine module 728 accesses metadata associated with the hierarchicalindex structure to retrieve a number of index nodes for each level ofthe index structure. As yet another example, the determine module 728determines that the index structure includes too few layers (e.g., depthis too small). The determine module 728 may output level information 738to indicate whether to expand the hierarchical ordered index structure.

With regards to the expanding the hierarchical ordered index structure,the expand module 730 enters a loop performing a series of expandingloop steps. In a first expanding loop step, the expand module 730retrieves the root index node of the hierarchical ordered indexstructure from the set of DSN storage servers 724 of the DSN 722. Forexample, the expand module 730 generates slice requests 740 based on theDSN address of the root index node, outputs the slice requests to theset of DSN storage nodes 724, receives slice responses 742 that includesat least a decode threshold number of encoded root index node slices andthe revision number indicator associated with the slices, and decodesthe at least the decode threshold number of encoded root index nodeslices to reproduce the root index node. In a second expanding loopstep, the expand module 730 identifies immediate children index nodes ofthe root index node from entries of the root index node.

In a third expanding loop step, the expand module 730 divides theimmediate children index nodes into sets of children index nodes. Theexpand module 730 functions to divide the immediate children index nodesinto sets of children index nodes by determining a number of sets thatthe immediate children index nodes will be divided into (e.g., toachieve goals for number of entries per node and/or number of nodes perlevel) and based on the number of sets and the ordering of attributes ofan attribute category, dividing the immediate children index nodes intothe sets of children index nodes.

In a fourth expanding loop step, the expand module 730 creates, for eachof the sets of children index nodes, a sub-root index node to produce aset of sub-root index nodes, wherein the sub-root index node includesentries for each child index node of the corresponding set of childrenindex nodes. For example, the expand module 730 creates the sub-rootindex node to include the entries (e.g., DSN address, minimum index key)for each of the child index nodes and a sibling entry (e.g., DSNaddress, minimum index key) for a sibling sub-root index node when asibling sub-root index node exists within the index structure to theright.

In a fifth expanding loop step, the expand module 730 creates a new rootindex node to include entries (e.g., DSN address, minimum index key) foreach of the sub-root index nodes of the set of sub-root index nodes. Theexpand module 730 functions to create the new root index node to includeentries for each of the sub-root index nodes of the set of sub-rootindex nodes by creating a first entry for a first sub-root index node ofthe set of the sub-root index nodes to include a first index key and afirst DSN address and creating a second entry for a second sub-rootindex node of the set of the sub-root index nodes to include a secondindex key and a second DSN address.

In a sixth expanding loop step, the expand module 730 temporarily storesthe new root index node and the set of sub-root index nodes in the DSN.For example, for each node of the new root index node and the set ofsub-root index nodes, the expand module 730 encodes the node using adispersed storage error encoding function to produce a set of encodednew root index node slices, generates a set of write slice requests 740that includes the set of encoded new root index node slices, and outputsthe set of slice requests 740 to the set of DSN storage nodes 724.

When the root index node has changed, in a seventh expanding loop step,the expand module 730 repeats the loop with the changed root index nodebeing the root index node. The expand module 730 functions to determinethat the root index node has changed by utilizing a checked-writeprocess of the DSN with respect to the root index node (e.g., a revisionlevel has changed, which is indicated in a response from the DSN 722 andlocks down processing for the new root index node). Alternatively, theexpand module 730 functions to determine that the root index node haschanged by re-retrieving the root index node to produce a re-retrievedroot index node, comparing the re-retrieved root index node with theroot index node, and when the re-retrieved root index node substantiallymatches the root index node, indicating that the root index node has notchanged. The expand module functions to repeat the loop by, prior torepeating the loop, deleting the temporary storage of the new root indexnode and the set of sub-root index nodes in the DSN (e.g., issuerollback requests to the set of DSN storage servers 724 for each set ofwrite requests) and undoing the creating of the new root index node(e.g., delete from local memory to facilitate starting over), thecreating of the set of sub-root index nodes, and the dividing of theimmediate children index nodes into the sets of children index nodes.

Alternatively, when the root index node has not changed, in the seventhexpanding loop step, the expand module 730 updates the hierarchicalordered index structure with the new root index node and the set ofsub-root index nodes. The expand module 730 functions to update thehierarchical ordered index structure by utilizing a three-phase-commitwrite process to store the new root index node and the set of sub-rootindex nodes in the DSN, where, when the three-phase-commit write processis successfully executed, the hierarchical ordered index structure isupdated (e.g., issue commit and finalize requests to the set of DSNstorage servers for each set of write requests).

FIG. 46H is a flowchart illustrating an example of expanding an index.The method begins at step 750 where a processing module (e.g., of acomputer of a multiple computer dispersed storage network (DSN) thatstores a plurality of data objects) determines to expand a hierarchicalordered index structure of a plurality of data object index entries(e.g., data object index key and DSN address of a corresponding dataobject). The hierarchical ordered index structure is in accordance withan ordering of attributes of an attribute category and the plurality ofdata object index entries is stored in a multitude of data object levelindex nodes and is associated with the plurality of data objects. Thedetermining to expand the hierarchical ordered index structure includesat least one of a variety of determining approaches. A first determiningapproach includes determining that the root index node includes too manyentries of the immediate children nodes (e.g., the determining includescounting the number of child node entries and comparing to a child nodeentry maximum threshold). The immediate children index nodes includesintermediate index nodes of the hierarchical ordered index structure,where the intermediate index nodes hierarchically lie between the rootindex node and the data object level index nodes. The immediate childrenindex nodes further includes at least some of the multitude of dataobject level index nodes.

A second determining approach includes determining that the root indexnode is being accessed too frequently in a given time frame. A thirddetermining approach includes receiving a request (e.g., after addingseveral data object entries associated with several data objectscorresponding to the hierarchical ordered index structure). A fourthdetermining approach includes detecting that an access performance levelof the hierarchical ordered index structure compares unfavorably to adesired access performance level threshold (e.g., unfavorable whenaccess performance level is less than the minimum access performancelevel threshold). A fifth determining approach includes determining anestimated growth rate of the hierarchical ordered index structure andexpanding in accordance with the estimated growth rate (e.g., growingfast as detected by identifying a level of the structure that includestoo many index nodes or an index node includes too many entries or thestructure includes too many layers, prepare of growth in advance).

When the hierarchical ordered index structure is to be expanded, themethod continues at step 752 where the method enters a loop thatincludes retrieving a root index node of the hierarchical ordered indexstructure from a set of DSN storage servers of the DSN. For example, theprocessing module obtains a DSN address of the root index node from ametadata table for the index structure, issues retrieval commands to theset of DSN storage servers using the DSN address, receives at least adecode threshold number of encoded root index slices, identifies arevision number of the encoded root index slices, and decodes the atleast the decode threshold number of encoded root index slices toreproduce the root index node.

The method continues at step 754 where the processing module identifiesimmediate children index nodes of the root index node from entries ofthe root index node. The method continues at step 756 where theprocessing module divides the immediate children index nodes into setsof children index nodes. The dividing the immediate children index nodesinto sets of children index nodes includes determining a number of setsthat the immediate children index nodes will be divided into (e.g., toachieve goals for number of entries per node and/or number of nodes perlevel) and based on the number of sets and the ordering of attributes ofan attribute category, dividing the immediate children index nodes intothe sets of children index nodes.

The method continues at step 758 where the processing module creates,for each of the sets of children index nodes, a sub-root index node toproduce a set of sub-root index nodes, where the sub-root index nodeincludes entries for each child index node of the corresponding set ofchildren index nodes. The method continues at step 760 where theprocessing module creates a new root index node to include entries foreach of the sub-root index nodes of the set of sub-root index nodes. Thecreating the new root index node to include entries for each of thesub-root index nodes of the set of sub-root index nodes includescreating a first entry for a first sub-root index node of the set of thesub-root index nodes to include a first index key and a first DSNaddress and creating a second entry for a second sub-root index node ofthe set of the sub-root index nodes to include a second index key and asecond DSN address. The method continues at step 762 where theprocessing module temporarily stores the new root index node and the setof sub-root index nodes in the DSN (e.g., issuing write slice requestsbut not commit and finalize requests).

The method continues at step 764 where the processing module determineswhether the root index node has changed. The determining that the rootindex node has changed includes utilizing a checked-write process of theDSN with respect to the root index node (e.g., detecting a revisionlevel has changed since reading the root index node, which is indicatedin a response from the DSN). Alternatively, the determining that theroot index node has changed includes a series of detecting steps. Afirst detecting step includes re-retrieving the root index node toproduce a re-retrieved root index node. A second detecting step includescomparing the re-retrieved root index node with the root index node.When the re-retrieved root index node substantially matches the rootindex node, a third detecting step includes indicating that the rootindex node has not changed. The method branches to step 772 when theprocessing module determines that the root index node has not changed.The method continues to step 766 when the processing module determinesthat the root index node has changed.

When the root index node has changed, the processing module repeats theloop to start over. Prior to repeating the loop, the method continues atstep 766 where the processing module deletes the temporary storage ofthe new root index node and the set of sub-root index nodes in the DSN(e.g., issue rollback requests to the set of DSN storage servers foreach set of write requests). The method continues at step 768 where theprocessing module performs undoing of the creating of the new root indexnode (e.g., delete from local memory to facilitate starting over), thecreating of the set of sub-root index nodes, and the dividing of theimmediate children index nodes into the sets of children index nodes.The method continues at step 770 where the processing module repeats theloop with the changed root index node being the root index node as themethod branches back to step 752.

When the root index node has not changed, the method continues at step772 where the processing module updates the hierarchical ordered indexstructure with the new root index node and the set of sub-root indexnodes. The updating the hierarchical ordered index structure includesutilizing a three-phase-commit write process to store the new root indexnode and the set of sub-root index nodes in the DSN, wherein, when thethree-phase-commit write process is successfully executed, thehierarchical ordered index structure is updated. For example, theprocessing module issues commit and finalize requests to the set of DSNstorage servers for each set of write requests.

FIG. 47 is a flowchart illustrating an example of acquiring operationalsoftware. The method begins at step 780 where a processing module (e.g.,of a computing device of a distributed storage and task network (DSTN)detects a boot operation. The detection includes at least one ofreceiving a message, detecting a reset, receiving a response from aquery, detecting an error, and initiating a reset request. The methodcontinues at step 782 where the processing module retrievesinitialization information from a local memory (e.g., of the computingdevice). The initialization information includes one or more of a devicetype indicator (e.g., a distributed storage and task (DST) executionunit, a DST processing unit, a user device, etc.), boot dispersedstorage error coding function software, a boot DSTN (ID), a boot DSTNinternet protocol address, and a security credential.

The method continues at step 784 where the processing module accesses aboot DSTN module utilizing the initialization information. For example,the processing module generates an access request that includes the bootDSTN ID, the security credential, and the device type indicator, andsends the access request to the boot DSTN internet protocol address. Themethod continues at step 786 where the processing module retrievesoperational software slices and configuration slices from the DSTNmodule. For example, the processing module generates at least a readthreshold number of read slice requests that includes slice namesassociated with the operational software slices and the configurationslices, sends the at least a read threshold number of read slicerequests to the DSTN module, and receives at least a decode thresholdnumber of operational software slices and at least a decode thresholdnumber of configuration slices from the DSTN module.

The method continues at step 788 where the processing module decodes theoperational slices and configuration slices to produce operationalsoftware and configuration information. The operational softwareincludes executable code to perform one or more functions in accordancewith the device type. The configuration information includes hardwareand software configuration parameters to enable the computing device toperform in accordance with the device type. For example, the processingmodule utilizes the dispersed storage error coding function software ofthe initialization information to decode the at least a decode thresholdnumber of operational software slices to produce the operationalsoftware and the processing module utilizes the dispersed storage errorcoding function software to decode the at least a decode thresholdnumber of configuration slices to produce the configuration information.

The method continues at step 790 where the processing module installsand executes the operational software. The installing includesconfiguring the computing device in accordance with the configurationinformation. The method continues at step 792 where the processingmodule selects a DSTN. The selecting includes identifying a DSTN from alist of one or more DSTNs (e.g., from the configuration information,from the initialization information, from a received message) inaccordance with one or more of the initialization information, userinput, a request, and a message.

The method continues at step 794 where the processing module sends aregistration request to the selected DSTN. The sending includesgenerating a registration request that includes one or more of thedevice type indicator, a device ID, a DSTN ID, and security credentials.The method continues at step 796 where the processing module receivesregistration information from the selected DSTN. The registrationinformation includes one or more of the device ID, the DSTN ID, updateddispersed storage error coding function software, and updated securitycredentials.

FIG. 48A is a schematic block diagram of another embodiment of adistributed computing system that includes a distributed storage andtask network (DSTN) managing unit 18 and a plurality of computingdevices 1-9 located at a plurality of sites 1-3. The DSTN managing unit18 and the plurality of sites 1-3 are operably coupled to each other viaa wide area network (WAN) 802 and each computing device at each site isoperably coupled to other computing devices at a common site via atleast one of local area network (LAN) 804, 806, and 808. A task of theDSTN managing unit 18 includes distributing a software image 800 to eachcomputing device of the computing system. For example, the DSTN managingunit 18 functions to distribute the software image 800 to each computingdevice of the plurality of computing devices 1-9 such that eachcomputing device can install and execute the software image 800 toparticipate in the operation of the DSTN computing system.

The DSTN managing unit 18 partitions the software image 800 into a setof image partitions A-C to distribute the software image 800 inpartitions. The DSTN managing unit 18 may partition software image 800utilizing a dispersed storage error coding function to encode thesoftware image 800 into the set of image partitions A-C. For example,the DSTN managing unit 18 determines a number of image partitions to besubstantially the same as the number of sites of the system thatincludes the computing devices 1-9. For instance, the DSTN managing unit18 partitions the software image into the three image partitions A-Cwhen the computing system includes computing devices deployed at thethree sites 1-3. The DSTN managing unit 18 sends each partition of theset of image partitions A-C to a corresponding site of the plurality ofsites 1-3. For example, the DSTN managing unit 18 sends image partitionA to computing device 1 at site 1, sends image partition B to computingdevice 5 at site 2, and sends image partition C to computing device 9 atsite 3. The sending of the software image 800 as one transmission ofeach image partition provides a WAN bandwidth utilization improvementsince multiple copies of the software image are not sent, via the WAN802, from the DSTN managing unit 18 to each computing device.

At least one computing device at each site receives a correspondingimage partition, stores the image partition in a local memory of thecomputing device, sends the image partition to at least one othercomputing device at a common site via a corresponding LAN, and sends theimage partition to at least one other computing device of at least oneother site via WAN 802. The sending the image partition to the at leastone other computing device of at least one other site includes sendingthe image partition directly to the at least one other computing deviceand sending the image partition via a second computing device to the atleast one other computing device. For example, computing device 1receives image partition A, stores the image partition A in a localmemory of computing device 1, sends the image partition A to computingdevice 2, sends the image partition A to computing device 3, sends theimage partition A to computing device 4 at site 2, and sends the imagepartition A to computing device 7 at site 3 via computing device 4. Asanother example, computing device 5 receives image partition B, storesthe image partition B in a local memory of computing device 5, sends theimage partition B to computing device 4, sends the image partition B tocomputing device 6, sends the image partition B to computing device 3 atsite 1, and sends the image partition B to computing device 9 at site 3(e.g., directly). The sending of the software image between computingdevices at a common site via the LAN provides a WAN bandwidthutilization improvement. A method to partition the software image 800and distribute the software image partitions is discussed in greaterdetail with reference to FIGS. 48B-48C.

FIG. 48B is a flowchart illustrating an example of issuing a softwareimage update. The method begins at step 810 where a processing module(e.g., of a distributed storage and task network (DSTN) managing unit)identifies computing devices at a set of sites requiring a softwareimage update. The identifying includes at least one of receiving anerror message, receiving a reboot message, receiving a software versionquery, receiving a request, and receiving a software rebuildingindication. The method continues at step 812 where the processing modulepartitions a software image update to produce a set of software imagepartitions. The partitioning includes determining a number of softwareimage partitions based on one or more of a number of the set of sitesassociated with the computing devices, a number of computing devices, aprevious software image update sequence, an error message, a priorityindicator, and a wide area network (WAN) performance indicator. Forexample, the processing module determines the number of software imagepartitions to be substantially the same as the number of the set ofsites.

The method continues at step 814 where the processing module, for eachsite, selects a computing device to send a corresponding software imagepetition of the set of software image partitions. The selecting may bebased on one or more of a computing device performance indicator, a WANroute performance indicator, a predetermination, and look up, and asoftware image partition request. For example, the processing moduleselects a first computing device at a first site to receive a firstsoftware image partition when the first computing device is associatedwith a favorable computing device performance indicator. The methodcontinues at step 816 where the processing module, for each site, sends(e.g., via the WAN) the corresponding software image partition to theselected computing device.

FIG. 48C is a flowchart illustrating an example of receiving a softwareimage update. The method begins at step 818 where a processing module(e.g., of computing device of a distributed storage and task network(DSTN)) receives a software image partition of a set of software imagepartitions (e.g., via a wide area network (WAN) or a local area network(LAN)). The method continues at step 820 where the processing modulestores the software image partition in a local memory (e.g., associatedwith the computing device). The method continues at step 822 where theprocessing module, when receiving the software image partition fromanother site, forwards the software image partition to one or more othercomputing devices at a common local site requiring a software imageupdate. The forwarding includes identifying the one or more othercomputing devices at the local site requiring the software image update.The identifying includes at least one of initiating a query, receiving aquery response, receiving a software image partition request, accessingLAN registration information, and a lookup.

The method continues at step 824 where the processing module, whenreceiving the software image partition from a primary source (e.g., froma DSTN managing unit), forwards the software image partition to at leastone computing device at one or more remote sites requiring a softwareimage update. The forwarding includes identifying the at least onecomputing device at the one or more remote sites requiring the softwareimage update. The identifying includes at least one of initiating aquery via the WAN, receiving a query response via the WAN, receiving asoftware image partition request via the WAN, accessing WAN registrationinformation, and a lookup. The forwarding further includes identifyingan intermediary computing device to forward the software image partitionto the at least one computing device. The identifying the intermediarycomputing device may be based on one or more of a WAN topology, a WANperformance indicator, a candidate intermediary computing deviceperformance indicator, a query, and a lookup.

The method continues at step 826 where the processing module receivesand aggregates the set of software image partitions to reproduce asoftware image update. The receiving may include verifying integrity ofthe software image update. The method continues at step 828 where theprocessing module activates the software image update. The activatingincludes at least one of loading the software image update, configuringthe computing device in accordance with the software image update,registering the software image update (e.g., sending a registrationrequest to a DSTN managing unit) and initializing execution of at leasta portion of the software image update.

FIG. 49A is a flowchart illustrating an example of preparing for anupgrade. The method begins at step 830 where a processing module (e.g.,of a distributed storage and task (DST) client module) obtainsdistributed storage and task network (DSTN) address range informationfor each memory device of a set of memory devices associated with a DSTexecution unit targeted for an upgrade (e.g., a software upgrade, ahardware upgrade). The obtaining includes at least one of receiving theaddress range information, performing a lookup in a DSTN address tophysical location table, generating a address range request, retrievingan address range of a previous upgrade, and initiating a query.

The method continues at step 832 where the processing module obtains alist of at least some slice names associated with slices stored in eachmemory device of the DST execution unit. The obtaining includesselecting a portion of the address range based on the DSTN address rangeinformation, generating a list request, sending the list request to theDST execution unit, and receiving a list response from the DST executionunit that includes the list of at least some of the slice names. Themethod continues at step 834 where the processing module retrieves theslices associated with the at least some of the slice names. Theretrieving includes generating read slice requests that includes theslice names, sending the resource requests to the DST execution unit,and receiving the slices from the DST execution unit. The slices mayinclude two or more slices for each slice name when more than onerevision is associated with the slice name.

The method continues at step 836 where the processing module generatesan integrity check value for the slices. For example, the processingmodule utilizes a hashing function to produce a hash value over theslices as the integrity check value. The method continues at step 838where the processing module stores one or more of the integrity checkfailure, the slices, and the slice names as integrity information in alocal memory (e.g., in one or more of the DST execution unit, a memoryassociated with a DST client module, in one or more other DST executionunits). The method continues at step 840 where the processing moduleenables an upgrade sequence of the DST execution unit. The enabling mayinclude caching new slices being written to the DST execution unit priorto the upgrade sequence. The caching may include sending the new slicesto an alternate DST execution unit.

FIG. 49B is a flowchart illustrating an example of verifying an upgrade,which include similar steps to FIG. 49A. The method begins with step 842where a processing module (e.g., of a distributed storage and task (DST)client module) retrieves integrity information for a DST execution unitas retrieved integrity information. The retrieving includes identifyinga storage location and sending a bigger information retrieval request tothe storage location. The method continues with the steps 830-836 ofFIG. 49A where the processing module obtains distributed storage andtask network (DSTN) address range information for each vault of eachmemory device of a set of memory devices of the DST execution unit(e.g., utilizing slice names of the retrieved integrity information),obtains a list of at least some slice names associated with slicesstored on each memory device of the DST execution unit, retrieves theslices associated with the at least some of the slice names, andgenerates an integrity check value for the slices.

The method continues at step 844 where the processing module combinesthe integrity check value, the slices, and the slice names as integrityinformation. The method continues at step 846 where the processingmodule, when integrity information compares unfavorably to the retrievedintegrity information, indicates an unfavorable condition. Theprocessing module determines that the integrity information comparesunfavorably to the retrieved integrity information when an integritycheck value is not substantially the same as the retrieved integritycheck value for at least one memory device. In addition, the processingmodule may initiate a rebuilding process when the integrity informationcompares unfavorably to the retrieved integrity information.Alternatively, when the processing module determines that the integrityinformation compares favorably to the retrieved integrity information,the processing module generates an indication that the upgrade wassuccessful with respect to not corrupting slices stored in the DSTexecution unit.

FIG. 50A is a flowchart illustrating an example of migrating an encodeddata slice. The method begins with step 848 where a processing module(e.g., of a source distributed storage and task (DST) execution unit)identifies a slice name corresponding to a slice to migrate from thesource DST execution unit to a destination DST execution unit. Theidentifying includes at least one of receiving a request, receiving theslice name, receiving an error message, detecting a unfavorable memorycondition (e.g., too full), performing a lookup, and receiving a memorytest result. The method continues at step 850 where the processingmodule sends the slice to migrate to the destination DST execution unit.The sending includes sending the slice name to the DST execution unit.

The method continues at step 852 where the processing module generates aslice verification request and sends the slice certification request tothe destination DST execution unit. The generating includes generatingthe request to include one or more of the slice name, the slice, arevision indicator, a verification method indicator (e.g., utilize ahashing function, utilize a signature function), and a nonce. The methodcontinues at step 854 where the processing module receives an integrityvalue from the destination DST execution unit. The integrity valueincludes at least one of a hashing function hash result (e.g., a hashover the slice and the nonce) and a signed package, wherein the packageincludes the slice and the nonce.

The method continues at step 856 where the processing module determineswhether the integrity value compares favorably to the slice verificationrequest. For example, the processing module determines that theintegrity value compares favorably to the slice verification requestwhen a hash of the slice and nonce is substantially the same as theintegrity value when the integrity value includes the hash result. Asanother example, the processing module determines that the integrityvalue compares favorably to the slice verification request when adecrypted signature (e.g., utilizing a public key of the destination DSTexecution unit) of the integrity value is substantially the same as atleast one of a hash of the slice and the nonce or the slice and thenonce when the integrity value includes the signed package. The methodrepeats back to step 850 when the processing module determines that theintegrity value compares unfavorably to the slice verification request.The method continues to step 858 when the processing module determinesthat the integrity value compares favorably to the slice verificationrequest.

The method continues at step 858 where the processing module updates aslice name assignment with regards to the slice name. For example, theprocessing module associates the destination DST execution unit with theslice name and disassociates the source DST execution unit with theslice name in a physical location to distribute storage and task network(DSTN) address table. The method continues at step 860 where theprocessing module deletes the slice from the source DST execution unit.For example, the processing module sends a write slice request to thesource DST execution unit, where the request includes the slice name andan indication to delete the slice.

FIG. 50B is a flowchart illustrating an example of saving a migratedencoded data slice. The method begins with step 862 where a processingmodule (e.g., of a destination distributed storage and task (DST)execution unit) receives a slice to migrate from a source DST executionunit. The method continues at step 864 where the processing modulestores the slice to migrate in a memory device associated with thedestination DST execution unit. The storing includes storing a slicename associated with the slice to migrate in the memory device. Themethod continues at step 866 where the processing module receives aslice verification request from the source DST execution unit.

The method continues at step 868 where the processing module generatesan integrity value utilizing the slice to migrate and a nonce of theslice verification request based on a verification method indicator ofthe request. For example, the processing module performs a hashingfunction on the slice and the nonce to produce a hash result asintegrity value when the verification method indicator indicates toproduce a hash result. As another example, the processing modulegenerates a signature utilizing a private key associated with thedestination DST execution unit over the slice and the nonce to produce asigned package as the integrity value when the verification methodindicator indicates to produce a signature. The method continues at step870 where the processing module sends the integrity value to the sourceDST execution unit.

FIG. 51A is a diagram illustrating an example of a registry structure872. The registry structure 872 may be utilized to provide registryinformation to a plurality of units and devices associated with at leastone of a distribute storage and task network (DSTN) and a dispersedstorage network (DSN). The registry structure includes a global registrystructure 874 and one or more storage pool sub-registries 1-S. Theglobal registry structure 874 includes an access control list (ACL)field 876, a realm information field 878, a vault information field 880,a vault permissions field 882, and a storage pool list field 884. TheACL field 876 includes one or more ACL entries that specify which useridentifier (ID) and or vault ID may access which devices and services ofthe DSTN. The realm information field 878 includes one or more realminformation entries, where a realm information entry identifies one ormore of an internet naming address, contact information, and a realm tovault ID map. The vault information field 880 includes one or more vaultinformation entries, where a vault information entry includes one ormore of a vault ID, a memory allocation maximum, a memory allocationminimum, and a storage pool association. The vault permissions field 882includes one or more vault permission entries, where a vault permissionentry includes network element IDs and services of the DSTN that areaccessible by users of the vault. The storage pool list field 884includes one or more storage pool entries, where a storage pool entryincludes a list of distributed storage and task (DST) execution unit IDsof a common storage pool ID and one or more vault IDs associated withthe storage pool ID. The global registry structure 874 may be utilizedby each unit, device, and module of the DSTN to access the DSTN andutilize services of the DSTN.

A number of storage pool sub-registry 1-S corresponds to a number ofstorage pools that are assigned to one or more vaults. Each storage poolsub-registry includes a namespace assignments field 886. The namespaceassignments field 886 includes one or more namespace assignment entries,where a namespace assignment entry includes a DSTN address rangeassignment corresponding to the storage pool of DST execution units.Each storage pool sub-registry is sent to units, devices, and modulesassociated with the corresponding storage pool (e.g., by vaultassociation). For example, storage pool sub-registry 1 is sent to DSTexecution units 1-5 of a storage pool 1 and storage pool sub-registry 2is sent to DST execution units 6-10 of a storage pool 2. In an exampleof operation, an element of a storage pool sub-registry is updated,affected DST execution units are identified based on the storage poolsub-registry, and the updated storage pool sub-registry is sent to theaffected DST execution units (e.g., without the need to send the globalregistry structure too).

FIG. 51B is a flowchart illustrating an example of distributing registryinformation. The method begins with step 888 where a processing module(e.g., of a distributed storage and task network (DSTN) managing unit)generates a global registry for a plurality of vaults. The generatingincludes generating the global registry based on one or more ofpreprogrammed vault information, a user input, a manager input, a vaultlist, a storage pool configuration output, a list of distributed storageand task (DST) execution units, billing information, a defaultauthorization list, internet information, and a network managementtemplate. The method continues at step 890 where the processing modulefacilitates sending the global registry to each unit of a DSTN. Forexample, the processing module sends the global registry to a publishingserver, where the publishing server forwards the global registry to eachunit of the DSTN.

The method continues at step 892 where the processing module generates aplurality of sub-registries, where each sub-registry corresponds to oneor more vaults. The generating may be based on one or more of the globalregistry, a storage pool identifier (ID) to vault ID input list, a userinput, a sub-registry template, a manager input, a DST execution unitperformance indicator, and a vault list. The generating may furtherinclude updating an existing sub-registry to include updatedsub-registry information. The method continues at step 894 where theprocessing module, for each sub-registry, identifies one or more units,modules, and/or devices associated with the sub-registry. Theidentifying may be based on one or more of the global registry, a userinput, registration information, a lookup, and the message. The methodcontinues at step 896 where the processing module, for eachsub-registry, facilitates sending the sub-registry to the one or moreunits, modules, and/or devices associated with the sub-registry.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a processing module ofa computer of a multiple computer dispersed storage network (MCDSN) thatstores a plurality of data objects, the method comprises: determining toexpand a hierarchical ordered index structure of a plurality of dataobject index entries, wherein the hierarchical ordered index structureis in accordance with an ordering of attributes of an attributecategory, and wherein the plurality of data object index entries isstored in a multitude of data object level index nodes and is associatedwith the plurality of data objects; and when the hierarchical orderedindex structure is to be expanded, entering a loop that includes:retrieving a root index node of the hierarchical ordered index structurefrom a set of MCDSN storage servers of the MCDSN; identifying immediatechildren index nodes of the root index node from entries of the rootindex node; dividing the immediate children index nodes into sets ofchildren index nodes; creating, for each of the sets of children indexnodes, a sub-root index node to produce a set of sub-root index nodes,wherein the sub-root index node includes entries for each child indexnode of the set of children index nodes; creating a new root index nodeto include entries for each of the sub-root index nodes of the set ofsub-root index nodes; temporarily storing the new root index node andthe set of sub-root index nodes in the MCDSN; when the root index nodehas not changed, updating the hierarchical ordered index structure withthe new root index node and the set of sub-root index nodes; and whenthe root index node has changed, repeating the loop with the changedroot index node being the root index node.
 2. The method of claim 1,wherein the determining to expand the hierarchical ordered indexstructure comprises at least one of: determining that the root indexnode includes too many entries of the immediate children index nodes;determining that the root index node is being accessed too frequently ina given time frame; receiving a request; detecting that an accessperformance level of the hierarchical ordered index structure comparesunfavorably to a desired access performance level threshold; anddetermining an estimated growth rate of the hierarchical ordered indexstructure and expanding in accordance with the estimated growth rate. 3.The method of claim 1, wherein the immediate children index nodescomprise: intermediate index nodes of the hierarchical ordered indexstructure, wherein the intermediate index nodes hierarchically liebetween the root index node and the multitude of data object level indexnodes.
 4. The method of claim 1, wherein the immediate children indexnodes comprise: at least some of the multitude of data object levelindex nodes.
 5. The method of claim 1, wherein the dividing theimmediate children index nodes into the sets of children index nodescomprises: determining a number of sets that the immediate childrenindex nodes will be divided into; and based on the number of sets andthe ordering of attributes of the attribute category, dividing theimmediate children index nodes into the sets of children index nodes. 6.The method of claim 1, wherein the creating the new root index node toinclude entries for each of the sub-root index nodes of the set ofsub-root index nodes comprises: creating a first entry for a firstsub-root index node of the set of sub-root index nodes to include afirst index key and a first MCDSN address; and creating a second entryfor a second sub-root index node of the set of sub-root index nodes toinclude a second index key and a second MCDSN address.
 7. The method ofclaim 1 further comprises: determining that the root index node haschanged by utilizing a checked-write process of the MCDSN with respectto the root index node.
 8. The method of claim 1 further comprises:determining that the root index node has changed by: re-retrieving theroot index node to produce a re-retrieved root index node; comparing there-retrieved root index node with the root index node; and when there-retrieved root index node substantially matches the root index node,indicating that the root index node has not changed.
 9. The method ofclaim 1, wherein the updating the hierarchical ordered index structurecomprises: utilizing a three-phase-commit write process to store the newroot index node and the set of sub-root index nodes in the MCDSN,wherein, when the three-phase-commit write process is successfullyexecuted, the hierarchical ordered index structure is updated.
 10. Themethod of claim 1, wherein the repeating the loop comprises: prior torepeating the loop: deleting the temporary storage of the new root indexnode and the set of sub-root index nodes in the MCDSN; and undoing: thecreating of the new root index node; the creating of the set of sub-rootindex nodes; and the dividing of the immediate children index nodes intothe sets of children index nodes.
 11. A dispersed storage (DS) module ofa computing device of a multiple computing device dispersed storagenetwork (MCDDSN) that stores a plurality of data objects, the DS modulecomprises: a first module, when operable within the computing device,causes the computing device to: determine to expand a hierarchicalordered index structure of a plurality of data object index entries,wherein the hierarchical ordered index structure is in accordance withan ordering of attributes of an attribute category, and wherein theplurality of data object index entries is stored in a multitude of dataobject level index nodes and is associated with the plurality of dataobjects; and a second module, when operable within the computing deviceand when the hierarchical ordered index structure is to be expanded,causes the computing device to enter a loop that causes the computingdevice to: retrieve a root index node of the hierarchical ordered indexstructure from a set of MCDDSN storage servers of the MCDDSN; identifyimmediate children index nodes of the root index node from entries ofthe root index node; dividing the immediate children index nodes intosets of children index nodes; create, for each of the sets of childrenindex nodes, a sub-root index node to produce a set of sub-root indexnodes, wherein the sub-root index node includes entries for each childindex node of the set of children index nodes; create a new root indexnode to include entries for each of the sub-root index nodes of the setof sub-root index nodes; temporarily store the new root index node andthe set of sub-root index nodes in the MCDDSN; when the root index nodehas not changed, update the hierarchical ordered index structure withthe new root index node and the set of sub-root index nodes; and whenthe root index node has changed, repeat the loop with the changed rootindex node being the root index node.
 12. The DS module of claim 11,wherein the first module functions to determine to expand thehierarchical ordered index structure by at least one of: determiningthat the root index node includes too many entries of the immediatechildren index nodes; determining that the root index node is beingaccessed too frequently in a given time frame; receiving a request;detecting that an access performance level of the hierarchical orderedindex structure compares unfavorably to a desired access performancelevel threshold; and determining an estimated growth rate of thehierarchical ordered index structure and expanding in accordance withthe estimated growth rate.
 13. The DS module of claim 11, wherein theimmediate children index nodes comprise: intermediate index nodes of thehierarchical ordered index structure, wherein the intermediate indexnodes hierarchically lie between the root index node and the multitudeof data object level index nodes.
 14. The DS module of claim 11, whereinthe immediate children index nodes comprise: at least some of themultitude of data object level index nodes.
 15. The DS module of claim11, wherein the second module functions to divide the immediate childrenindex nodes into the sets of children index nodes by: determining anumber of sets that the immediate children index nodes will be dividedinto; and based on the number of sets and the ordering of attributes ofthe attribute category, dividing the immediate children index nodes intothe sets of children index nodes.
 16. The DS module of claim 11, whereinthe second module functions to create the new root index node to includeentries for each of the sub-root index nodes of the set of sub-rootindex nodes by: creating a first entry for a first sub-root index nodeof the set of sub-root index nodes to include a first index key and afirst MCDDSN address; and creating a second entry for a second sub-rootindex node of the set of sub-root index nodes to include a second indexkey and a second MCDDSN address.
 17. The DS module of claim 11 furthercomprises: the second module further functions to determine that theroot index node has changed by utilizing a checked-write process of theMCDDSN with respect to the root index node.
 18. The DS module of claim11 further comprises: the second module further functions to determinethat the root index node has changed by: re-retrieving the root indexnode to produce a re-retrieved root index node; comparing there-retrieved root index node with the root index node; and when there-retrieved root index node substantially matches the root index node,indicating that the root index node has not changed.
 19. The DS moduleof claim 11, wherein the second module functions to update thehierarchical ordered index structure by: utilizing a three-phase-commitwrite process to store the new root index node and the set of sub-rootindex nodes in the MCDDSN, wherein, when the three-phase-commit writeprocess is successfully executed, the hierarchical ordered indexstructure is updated.
 20. The DS module of claim 11, wherein the secondmodule functions to repeat the loop by: prior to repeating the loop:deleting the temporary storage of the new root index node and the set ofsub-root index nodes in the MCDDSN; and undoing: the creating of the newroot index node; the creating of the set of sub-root index nodes; andthe dividing of the immediate children index nodes into the sets ofchildren index nodes.